Eve Francotte
Stéphane Colognesi
Liesje Coertjens- Psychological Sciences Research Institute, UCLouvain, Louvain-la-Neuve, Belgium
Introduction: Educational inequalities – i.e., the achievement gaps between pupils from disadvantaged backgrounds and their peers from advantaged backgrounds – are present in many OECD countries. This is particularly problematic in reading, which is a predictor of future academic and social success. To reduce this reading achievement gap, recent meta-analyses point toward progress monitoring: regularly measuring pupils’ mastery levels and differentiating instruction accordingly. However, the research recommendations only slowly make their way to teaching habits, particularly because teachers may consider progress monitoring difficult and cumbersome to implement. To avoid such difficulties, partnerships between teachers and researchers have been recommended. These allow teachers’ complex realities to be taken into account and, consequently, tools to be designed that are meaningful and feasible for practitioners.
Method: Using an iterative and participatory process inspired by practice-embedded research, the present research set out to (1) co-construct tools to monitor first-graders’ progress in reading, and (2) examine how these tools met teachers’ needs. Five teachers in the French-speaking part of Belgium co-constructed four tools during four focus groups. The transcribed discussions were analyzed using an interactional framework containing three areas of knowledge: shared, accepted, and disputed.
Results and Discussion: The results indicated three shared needs: perceived usefulness, flexibility of the tools, and a desire to limit the workload. In addition, teachers accepted that, between them, needs varied regarding the goal for progress monitoring and the format of the evaluation. They had lengthy discussions on balancing workload and perceived utility, leading them to conclude that there were two groups of teachers. The first group questioned the added value of the progress monitoring tools in relation to their habitual practice. The second group on the other hand described the added value for the teacher, certainly when aiming to grasp the level and difficulties of struggling pupils. This second group had fewer years of teaching experience and described their classroom practice as less organized compared to the teachers from the first group. Theoretical and practical implications of these findings are discussed below.
1. Introduction
Multiple countries, such as France, Sweden, and Belgium, demonstrate strong educational inequalities, defined as the achievement gaps between pupils from disadvantaged and advantaged backgrounds (UNICEF Office of Research, 2016; Bricteux and Quittre, 2021). As reading predicts academic, professional, and social success (Slavin et al., 2011; Oslund et al., 2021), this deficit has important consequences for pupils from disadvantaged backgrounds. Yet, reducing educational inequality is far from easy: in the French-speaking part of Belgium for example, despite governmental policy to reduce this inequality, the PIRLS results from 2011 and 2016 indicate a widening of the achievement gap (Schillings et al., 2017).
The causes of this achievement gap were first explored via genetic or hereditary explanations, yet scientific studies do not confirm this hypothesis (for a review, see Nisbett et al., 2012). Currently, the most accepted explanation concerns children’s social and, particularly, home environments (Magnuson and Shager, 2010). Parents with higher educational attainment and more financial resources tend to provide their children with more conducive learning environments (Gennetian et al., 2010) and have higher academic expectations (Davis-Kean, 2005; Slates et al., 2012). More specifically regarding language learning, pupils from advantaged backgrounds were found to interact more with their parents and use a larger vocabulary (Hart and Risley, 2003; Davis-Kean, 2005). As pupils’ oral language skills are predictive of their reading skills (Le Normand et al., 2008; Bianco et al., 2012), this results in significant differences between pupils, even before they enter primary school (Magnuson and Shager, 2010). However, these differences are not deterministic. Indeed, on average, in the Organization for Economic Co-operation and Development (OECD), 11% of pupils have a resilient profile: they belong to the group of the 25% most disadvantaged pupils yet they reach the top 25% achievement in reading (Bricteux and Quittre, 2021). Furthermore, intervention studies have shown that it is possible to increase the reading skills of pupils from disadvantaged backgrounds (Dietrichson et al., 2017, 2021) and of struggling readers more specifically (Slavin et al., 2011; Neitzel et al., 2021). Dietrichson et al. (2017) conducted a meta-analysis of interventions for pupils from disadvantaged backgrounds. Among the teaching practices studied, progress monitoring appears promising (Hedges’ g = +0.32, 95% CI = [0.18; 0.47]) as does tutoring (Hedges’ g = +0.36, 95% CI = [0.26; 0.45]). However, the latter has a higher cost per pupil in time than teaching practices aimed at the whole class (Neitzel et al., 2021), such as progress monitoring.
These findings suggest that schools, and more particularly primary school teachers, can foster the reading achievement of at-risk pupils in their classrooms, and thus contribute to a decrease in educational inequalities. The purpose of this study is to create tools to monitor the progress in reading skills of early elementary pupils. To ensure that these tools are suitable for practice, they are created together with teachers. Furthermore, we examine how these tools meet the teachers’ needs when engaging in the progress monitoring of pupils’ reading skills.
1.1. Progress monitoring
Progress monitoring consists of regularly measuring and analyzing pupils’ progress with the aim of adapting the instruction to their needs (Dietrichson et al., 2017, 2021). This practice is at the heart of two other research strands that, despite common roots, have largely developed separately: formative assessment and data-driven decision making. Formative assessment are classroom practices where teachers and/or learners collect and interpret information about what and how pupils learn (Klute et al., 2017). According to Eysink and Schildkamp (2021), formative assessment consists of five main components: developing and sharing learning goals, collecting data about pupils’ learning, identifying pupils’ learning needs, acting appropriately on these in the classroom, and involving pupils in this process. Data-based Decision Making (also labeled Data-driven Decision Making) on the other hand is described as the continuous process of collecting and analyzing data about pupils’ skill level in order to guide decisions on instruction (Filderman et al., 2018; Schelling and Rubenstein, 2021).
Both formative assessment and Data-based Decision Making were found to be beneficial for reading skills. Indeed, Klute et al. (2017) report that formative assessment has a large effect on primary pupils’ reading achievement (+0.41 standard deviations on achievement level compared to a control group). In addition, the meta-analysis by Filderman et al. (2018) indicates that struggling readers benefit from Data-driven Decision Making (Hedges’ g = +0.27, 95% CI = [0.07, 0.47]). The literature also points to the fact that students learn more when tested than when they re-study the same material. This is referred to as the “test effect” (e.g., Adesope et al., 2017; Yang et al., 2021). Indeed, Yang et al. (2021) argue that tests were not only beneficial for learning factual knowledge, but also promoted conceptual learning and facilitated problem solving. More frequent testing may therefore be another benefit of progress monitoring.
1.1.1. Collecting and analyzing data on pupils’ learning
A key element of formative assessment concerns collecting data on pupils’ learning (Klute et al., 2017; Eysink and Schildkamp, 2021). This data can be either formal – such as exercises, homework assignments – or, informal – such as discussions with the pupil and observations of pupils while they are working on a classroom assignment (Gottheiner and Siegel, 2012; Hargreaves, 2013; Yin et al., 2014). Data can be collected by the learner (e.g., self-assessment), or by other pupils in the class (e.g., peer-assessment, Black and Wiliam, 2009). However, Klute et al. (2017) have shown that for reading, it is more effective to consider formative assessment delivered by a teacher, educator, or computer program.
Data-based Decision Making is more dependent on formal data (Wayman et al., 2012; Schildkamp, 2019; Eysink and Schildkamp, 2021; Hebbecker et al., 2022) and generally distinguishes between two types of measures: curriculum-based measurement (CBM) and mastery measures (Filderman et al., 2018; Filderman and Toste, 2018). CBMs are short, standardized measures that indicate the overall skill level. The most frequently measure used in reading is oral reading fluency, e.g., the number of words correctly read in one minute (Van Norman et al., 2018). Mastery measures are assessments of a specific skill – for example, decoding ability – and are closely linked to specific learning activities (Stecker et al., 2008). This helps to make decisions about the adjustment of the learning activity (Filderman and Toste, 2018; Van Norman et al., 2018). However, as mastery measures are specific and usually not norm referenced, they do not allow for the assessment of the overall skill and comparison with average pupils are limited (Filderman and Toste, 2018). Therefore, curriculum-based measures are recommended for detecting pupils at risk, while mastery measures are recommended for more regular progress monitoring of those pupils (Van Norman et al., 2018). Moreover, it is generally recommended to assess reading skills that pupils struggle with (Lemons et al., 2014).
Recommendations regarding frequency of measures of pupils’ progress vary (Ardoin et al., 2013). First, the baseline level of a pupil’s reading skills need to be assessed, based on three measures conducted in quick succession (Lemons et al., 2014; Filderman and Toste, 2018). After this step, the goal is to gain enough data to make relevant pedagogical decisions. Depending on the stability of growth of the pupils over time (Stecker et al., 2008), some researchers suggest a minimum of five to six weeks of data (Ardoin et al., 2013) while others suggest at least 20 moments of data collection (Christ et al., 2012). More precisely, Filderman and Toste (2018) recommend using more frequent data collection with struggling readers, as the deviation in their performance is larger and so the accuracy of the assessment is reduced.
After data collection, an analysis phase is needed to convert the raw data into useful information (Klute et al., 2017; Eysink and Schildkamp, 2021). Data can be presented in many forms: tables, texts or graphs (Hebbecker et al., 2022) When curriculum-based measurements is used, pupils’ results can be compared to a pre-established cut-off score or the slope can be analyzed to establish whether progress is in line with expectations (Filderman et al., 2018; Oslund et al., 2021). The aim of this analysis phase is to identify pupils’ strengths and weaknesses in order to best meet their needs (Eysink and Schildkamp, 2021).
1.1.2. Differentiation to support struggling pupils
When pupils’ progress is deemed insufficient, differentiation is recommended (Allal and Mottier Lopez, 2005; Klute et al., 2017; Filderman et al., 2018). Differentiation has been defined in many ways in the literature (Bondie et al., 2019; van Geel et al., 2019). Here, differentiation is defined as instructional adaptations in response to pupils’ cognitive needs (Roy et al., 2013; Deunk et al., 2018). It encompasses various practices such as homogenous flexible grouping or modifying learning instruction (Godor, 2021; Taylor et al., 2022). Regarding the effects of differentiation on pupils’ mathematics and reading performance, a recent meta-analysis concluded that there was a small, positive impact (Cohen’s d = +0.146, 95% CI = [0.066; 0.226]) (Deunk et al., 2018).
Additional evidence for the effect of progress monitoring and differentiation is presented in the studies on Response to Intervention (RTI) model (Puzio et al., 2020; Oslund et al., 2021). Various versions of the RTI model exist (Alahmari, 2019), but in its traditional form, the RTI model has three tiers, distinguished by the intensity of the support provided to the learner. Tier 1 concerns providing all pupils with the best possible evidence-based educational practices. Pupils showing difficulties are redirected to the second tier (Tier 2) where in addition to the instruction received in Tier 1, pupils receive a targeted intervention in small groups. Tier 3 is devoted to pupils with severe difficulties, which persist despite the Tier 2 intervention. These pupils receive a more intense and lengthy intervention, usually on a one-to-one basis (Greenfield et al., 2010; Alahmari, 2019; Neitzel et al., 2021).
Within each tier, the combination of high-quality teaching and regular assessment of pupils’ skill levels ensures effectiveness (Alahmari, 2019). As such, the RTI model is based on four critical components: the presence of different tiers, screening, progress monitoring, and data-based decision making (Oslund et al., 2021). Screening, implemented in Tier 1, identifies pupils at risk (Alahmari, 2019). Monitoring the progress of these pupils makes it possible to redirect them to Tier 2 and then Tier 3 if they do not show sufficient progress (Arden et al., 2017), to evaluate whether this extra support has the hoped-for results and, if not, to adjust the teaching practices of Tier 2 or Tier 3 (Alahmari, 2019).
Several studies highlight the benefits of the RTI model, while others are more skeptical. Thus, the advantages pinpointed in the literature are as follows. First, the RTI model allows for the identification of at-risk pupils as well as pupils in need of special education (Alahmari, 2019). Moreover, it enables pupils’ diverse needs to be addressed and interventions to be made as soon as difficulties arise (Arden et al., 2017; Alahmari, 2019). Moreover, data-based decision making brings more benefits to pupils with difficulties, as their progress is tracked on a regular basis (using mastery measures), allowing instruction to be tailored to their needs (Oslund et al., 2021). Meta-analyses by Slavin et al. (2011) and Neitzel et al. (2021) point to the positive effects of interventions with features of the RTI model on the reading skills of struggling pupils. However, others have suggested that RTI does not work as well as expected. Balu et al. (2015) collected data from 20,450 first through to third graders from 146 schools in the United States. The results showed that first graders who received interventions performed statistically worse than their peers. As for those in second and third grade who received a Tier 2 or Tier 3 intervention, they did not perform better than other students. Yet, as Al Otaiba et al. (2019) showed the findings of Balu et al. (2015) should be interpreted cautiously as many schools did not consistently implement RTI using evidence-based practices (e. g. Fuchs and Fuchs, 2017; Gersten et al., 2017).
1.1.3. Teachers’ difficulties in implementing progress monitoring and differentiation
Despite the clear benefits of progress monitoring and differentiation, these practices are underused, even in contexts that encourage their implementation (Oslund et al., 2021; Schelling and Rubenstein, 2021). Two key reasons appear to be particularly relevant. First, primary school teachers’ attitudes toward the RTI model (Greenfield et al., 2010; Castro-Villarreal et al., 2014; Cowan and Maxwell, 2015) and the data-driven decision making process (Schelling and Rubenstein, 2021) are mixed. While teachers perceive the usefulness and added value of these changes (Schelling and Rubenstein, 2021), particularly in tracking progress (Greenfield et al., 2010; Cowan and Maxwell, 2015), they find them a source of stress and anxiety (Schelling and Rubenstein, 2021), increasing their workload and responsibilities (Castro-Villarreal et al., 2014; Cowan and Maxwell, 2015). Indeed, teachers indicate a lack of time and resources for implementing these practices (Castro-Villarreal et al., 2014; Klute et al., 2017). Regarding differentiation, teachers identify several factors that hamper implementation: the diversity of students in the class group, the lack of support from the school, and, the lack of rich and useful information about students’ skills levels (van Geel et al., 2019). Teachers in general education face these challenges in particular, as they have on average more students per class, which makes it more difficult to implement small group instruction or individual support (Alahmari, 2019).
Secondly, teachers vary in their ability to collect data, interpret it and link this information to relevant pedagogical adaptations (Greenfield et al., 2010; Klute et al., 2017). Primary school teachers’ skills and perceived control were found to be related to their tendency to implement data-driven decision making (Prenger and Schildkamp, 2018). Nevertheless, it may be complicated for teachers to plan relevant pedagogical adaptations based on learners’ needs (Colognesi and Gouin, 2022) and to measure learners’ performance regularly before formal assessment (Gaitas and Alves Martins, 2017), as they are, for example, occupied with classroom management (Schelling and Rubenstein, 2021). In addition, the decision-making processes for differentiation are poorly documented in the literature (Puzio et al., 2020). For example, there are no clear recommendations on when a learner should be considered at risk and receive additional interventions (Hughes and Dexter, 2011). Some teachers report being uncertain about the boundaries between students who are expected to benefit from Tier 2 and those who are expected to benefit from Tier 3 (Greenfield et al., 2010). As a result, teachers are unprepared and unwilling to implement practices such as the RTI model and its implementation fidelity is low, with high variability between schools (Arden et al., 2017; Berkeley et al., 2020; Oslund et al., 2021). Without this fidelity, which is also recognized as important by teachers (Greenfield et al., 2010), the effects on student performance remain below expectations or are absent (Arden et al., 2017; Gersten et al., 2017).
1.2. Changing teaching practices
According to practice-embedded research (Donovan et al., 2013; Snow, 2015) and improvement science (Bryk, 2015, 2017), the gap between what is recommended in research and what is done in practice (Berkeley et al., 2020) is the result of research projects developing teaching programs that teachers should replicate (Bryk, 2015; Goigoux et al., 2021). Thus, researchers and teachers advocate increased in-service training to improve teaching practices (Castro-Villarreal et al., 2014; Cowan and Maxwell, 2015; Oslund et al., 2021). However, providing teachers with the latest scientific findings does not appear sufficient to bring about a change in teaching practices (Cèbe and Goigoux, 2018). Indeed, despite promising initial results, the effects of evidence-based practices may disappear when it is put into practice on a larger scale (Bianco, 2018; Bressoux, 2021).
Different hypotheses may explain this finding, such as the effects being less robust than expected or the degree to which teachers implement the program (Gersten et al., 2020). For various reasons, teachers may opt not to implement (part of) the program. First, the teachers do not want to, for example, because it is too costly to implement, it goes against their own experience or their own beliefs and conceptions regarding teaching and learning (Caena, 2011; Bressoux, 2021; Hanin et al., 2022), or they feel that their usual practices are no worse than what is being proposed and thus have little motivation to change (Quinn and Kim, 2017). Second, sometimes teachers cannot implement the program, because of external constraints, such as their school principal’s visions or the attitude of parents, who might, for example, see differentiated instruction as unequal treatment of pupils (Coppe et al., 2018). They may also believe that these new practices are not easily applicable to all classrooms, in all contexts (Quinn and Kim, 2017). Third, teachers may lack the necessary didactic skills and experience or else adequate implementation is difficult due to the absence of clear instructions (Bressoux, 2021). In other words, the implementation is hampered by the teaching program being poorly adapted to future users and the complex environments in which they work (Bryk, 2015).
Based on these findings, practice-embedded research aims to minimize the distance between researchers and teachers from the very beginning of the research process (Snow, 2015). There are two prerequisites: the need to consider the complex environment in which teachers practice and sustainable partnerships between researchers and practitioners (Snow, 2015). Indeed, the classroom is an environment that has become increasingly complex over time: teachers face more heterogeneous groups and interact with a variety of professionals, such as speech therapists or school psychologists (Bryk, 2015). To ensure that a program or tool is useful for practice, this complexity of the school environment needs to be integrated into the design process from the beginning (Class and Schneider, 2013). According to Snow (2015), to improve teaching practice, practitioners’ experience is just as valid a source of knowledge as scientific theories. The diversity of settings in which a teaching practice or tool is more or less effective allows the identification of the necessary conditions for the implementation (Class and Schneider, 2013) and may even provide an opportunity to explore factors for improvement (Bryk, 2015). So, rather than being constraints or variables that need to be controlled, the complexity of practice settings provides essential information (Snow, 2015). Thus, the use of the teaching practice in increasingly diverse settings allows for more insight into its effect and conditions (Bianco, 2018).
The second prerequisite concerns sustainable partnerships between teachers and researchers, which is indispensable for the latter to have access to the complex reality of teaching practice (Donovan et al., 2013). Thus, practice-embedded research promotes collaborative research in which researchers and practitioners are on an equal footing (Snow, 2015; Goigoux, 2017). Partnering with teachers from the start also allows for the development of tools that consider future users’ needs (Cèbe and Goigoux, 2018). Furthermore, it is essential that the tools or programs fit into the existing habits of the teachers (Goigoux et al., 2021).
Thus, rather than waiting for teachers to adapt their practice to researchers’ recommendations, the objective is to construct a program or a tool together, adapted to practitioners’ needs (Class and Schneider, 2013; Goigoux et al., 2021). For Goigoux et al. (2021), the quality of design predicts its acceptability among teachers and thus its implementation fidelity. Therefore, a properly designed tool should not require additional support from researchers when it is implemented.
1.3. The present study
To reduce the reading achievement gap, progress monitoring appears promising (Dietrichson et al., 2017). Yet, teachers consider this pedagogical practice difficult to implement (Castro-Villarreal et al., 2014) and recommendations from research only slowly make their way to practice (Berkeley et al., 2020). To create tools suitable for practice, practice-embedded research suggests considering the complexity of the teaching practice from the onset and building sustainable partnerships between teachers and researchers (Bryk, 2015; Snow, 2015; Goigoux, 2017).
Thus, we followed a group of elementary school teachers for 4 months. Using an iterative and participatory method, they co-created, with a reference researcher, tools to monitor the progress of early elementary school pupils in reading. Qualitative analysis of all discussions on this co-creation allows us to answer the following research question: how do the tools meet the needs of primary school teachers to monitor the progress of their pupils’ reading skills?
2. Methods
The method follows the recommendation of practice-embedded research (Snow, 2015) and educational design research (The Design-Based Research Collective, 2003). As advocated by Goigoux (2017) and Cèbe and Goigoux (2018) and used in similar research (e.g. Bogaerds-Hazenberg et al., 2019), we chose an iterative participatory process where a group of volunteer teachers work together to improve a prototype tool during focus groups. The prototype is then tested by the teachers in their classrooms and they bring their suggestions for improvement to the next meeting. These suggestions foster the development and improvement of the tool. Disagreements, either between teachers or between teachers and researchers, are especially discussed in order to work toward a common creation, both acceptable and feasible for all teachers and respecting the initial objectives of researchers.
2.1. Context of the study
As respective policies encouraged these practices, the Response to Intervention model is mainly present in the United States (Neitzel et al., 2021) and Data-based decision making is more studied in the Netherlands (Visscher, 2021). Our study takes place in the context of the French-speaking part of Belgium where such government recommendations do not exist. Furthermore, despite external tests and the dissemination of teaching guidelines, each teacher has a great degree of freedom to achieve the expectations set by the school curriculum (Dupriez, 1999; Renard et al., 2022). In this way, each teacher can select the textbooks, tools, materials, etc. that they use in their classroom.
2.2. Participants: the group of co-developers
The group of co-developers is composed of a reference researcher and five first and/or second-grade teachers from different schools. Table 1 provides an overview of their main characteristics. In addition to their teaching degree, all of them pursued or are pursuing other qualifications such as a Master’s in educational sciences. Furthermore, three teachers out of five have additional experience in teaching reading (e.g., co-authors of a teacher’s manual for reading and writing, and participation in a field experiment on the effect of co-teaching on pupils’ reading performance). The socio-economical background of their pupils ranged from highly disadvantaged to strongly advantaged.
Table 1. Overview of the participants and their main characteristics.
2.3. Procedure: focus group meetings between co-developers
The group of co-developers met four times between September 2021 and December 2021. The reference researcher1 organized the meetings and moderated the discussions. The first two meetings took place in Carol’s and Sophia’s classrooms, respectively. Due to the evolution of the COVID-19 pandemic, the last two focus groups were organized online. Table 2 provides an overview of the four focus groups and their main objectives.
Table 2. Summary of the focus groups.
The objectives of the first focus group were threefold. In the first place, the participants got to know each other and the guidelines for the collaborative work were agreed upon. Three aspects were discussed, as recommended by Van Nieuwenhoven and Colognesi (2015). First, the group dynamic: This involves aiming for a symmetrical relationship between the individuals, so that everyone would dare to share and could contribute according to their expertise. Second, the usefulness of collaborative work: what the group can bring to its members and to the teaching community as a whole. Third, the organizational aspects, involving discussing spatial (where the meetings take place) and temporal constraints (what are the most appropriate times for members, how to ensure that everyone is available). Then, a brief theoretical explanation of progress monitoring and the expected positive effects on pupils’ learning was provided. Mary, a doctoral student specializing in prerequisites for learning to read was present, was present, as an additional resource to introduce the theoretical background about learning to read. Finally, the reference researchers presented three tools available in the literature to measure pupils’ reading levels at the beginning of primary school: the assessments designed within the “PARLER” program (Zorman et al., 2015), the tool for identifying learning outcomes in reading in first grade (“OURA LEC/CP”, Billard et al., 2013), as well as the assessment sheets proposed in the “Reading Workshops” (Calkins, 2017). These tools include assessments of prerequisites and components of reading, such as phonological awareness, decoding, comprehension, fluency, and the concept of print. Teachers were also invited to bring in any useful resources. During the first focus group, participants provided consent to record future focus groups.
Between the first and second meetings, participants were invited to examine in more detail the tools brought by the reference researcher. Victoria also shared an extra resource from the government (Deum et al., 2007) and individual assessment sheets that she uses with her pupils.
During the second focus group, the group of co-developers work together to create the first prototype of the criterion-based rubric which aims to assess the skill level of a single pupil. They developed two versions: a landscape one, which allows multiple evaluations – and thus assesses the progress over time – and a portrait one, with a comment section.
Between the second and third focus groups, teachers were invited to use the tools in their classrooms. In addition, Sophia gave the tool to her colleague to get an external point of view. Based on these experiences, during the third focus group, the landscape version of the tool was seen as more useful. Hence, the portrait version was dropped. For the landscape version, some criteria were also adjusted. Furthermore, they developed a whole-class tool with the same criteria as the individual criterion-based rubric and a whole-class tool for letter-sound correspondence.
Again, between the third and the fourth meeting, teachers were invited to test the tools in their classrooms and Carol gave them to a colleague, in order to obtain additional suggestions. Based on their feedback, the group of developers discussed adaptations of the built tools. They also decided to provide a blank version of the whole class tool for letter-sound correspondence. Furthermore, they wrote the appendix for future users, mainly based on the suggestions of the two users who did not participate in the collaboration process. Finally, the group of co-developers shared their perceptions of the processes of collaboration.
2.4. Data analysis
To analyze how the developed tools met teachers’ needs when monitoring their pupils’ progress, we first transcribed more than 10 hours of audio-recorded discussions between the co-developers (for a total of 257 pages). Then, the focus group transcriptions were analyzed following the procedure described by Baribeau (2009). In the initial phase, the first author performed a first, inductive coding using the software Taguette. Next, the three authors discussed these codes. Based on the results of this discussion, the first author coded the transcripts anew to refine the coding and to categorize the codes into the main needs, which were discussed anew by the three authors.
Given that decisions made on the tools are the results of discussions between the developers, an interactionist framework was also chosen. The selected framework separates three areas of knowledge: shared, accepted, and disputed (Morrissette, 2011a; Morrissette and Guignon, 2016). Shared knowledge characterizes points of discussion on which participants agree. Accepted knowledge represents whatever received neither the absolute approval nor disapproval of participants. Disputed knowledge is the result of strong disagreements among the developers. Although this analytical framework was initially created to classify discussions between teachers about their teaching practices (Morrissette, 2011b), it has also been used in various contexts, generally in group interviews (e.g., Nadeau, 2021). With regard to the progress monitoring in reading at the beginning of elementary school, this framework highlights both what is shared, and therefore the professional routines into which the tools must be integrated, what is accepted, which may constitute avenues for improving teaching practices, and what is disputed, representing a probable obstacle for dissemination.
3. Results
3.1. Tools for progress monitoring
In line with the aim of developing tools to monitor first-grade pupils’ progress in reading, participating teachers and a researcher co-created tools that were improved as the focus groups progressed, based on teacher feedback. Thus, the co-creation process resulted in four tools (see Supplementary material). The first tool is a criterion-based rubric for reading components, targeting the progress of one pupil over time. It contains 28 criteria grouped into 5 categories: phonological awareness, rapid naming, phonics, global reading of function words, and understanding. The second tool is the whole-class version, allowing a teacher to assess all pupils in the classroom using one document. The third tool is a whole-class tool for letter/sound correspondence. Finally, a blank version is also suggested. In addition, the developers have written an appendix containing the instructions for use and some theoretical details.
3.2. Teachers’ needs to monitor progress in reading
To answer the research question on teachers’ needs when monitoring pupils’ progress, the analysis of discussions between the participating teachers revealed four important needs to be met by the constructed tools: perceived usefulness, limiting the workload, balancing workload and perceived usefulness, and flexibility. In line with the interactionist framework, these can be grouped into three areas of knowledge: shared, accepted, and disputed. Table 3 provides an overview of these results, which are further detailed below.
Table 3. Overview of results crossing participant needs with the interactionist framework.
3.2.1. Perceived usefulness
3.2.1.1. Shared area
The co-developers attempt to optimize the perceived usefulness of the tools by multiplying the objectives facilitated by them. Thus, participating teachers share three common goals: identify the level differences between pupils, differentiate and log information.
The tools allow for the observations of pupils’ progress over time, as they evidence the evolution of the number of criteria met. “And so, if we want to monitor progress, […] we have to be able to situate the pupil” (Sophia, Focus group 1, further abbreviated as FG1). Teachers who use the tools feel that the differences between pupils were identified and biases were avoided. As Mary points out (FG 1), “The risk in […] not identifying a pupils’ level is that sometimes […] we put him in a category by saying to oneself […] it’s not going okay. Whereas, if we had, if we had assessed the pupil’s level individually […] he might have been quite successful.”2 George notes the opposite risk: “And more seriously, the opposite is also true. Because … for example, a super shy pupil, I realized in December that he was struggling a lot while … [for] me, it was going okay” (FG 1).
Determining whether pupils master certain skills allows those at risk to be identified. Furthermore, it helps to identify which specific skills to target during differentiated instruction, which can take the form of for example additional exercises or additional time with the teacher alone or in small groups. Mary explains: “Here we can tick off, if we did not check off [the criteria], that means that these pupils must be worked with separately. But the others keep going and the pupils who have not yet acquired the skills keep going, but we thus saw it in time and we remedy in time. Lucy: That’s it.” The goal is to identify struggling pupils in time to offer them differentiated instruction, and so, prevent a widening of the gap between high and low performers.
Finally, the tool makes it possible to log all of the teachers’ reflections when they observe their pupils or analyze the results of a test. These notes indicate whether an error persists and can be used as a support for communication with parents, speech therapists, or other teachers. In addition, teachers can communicate this information to the pupils, using verbal or written feedback. The tools also make it possible to assess the effects of differentiated instruction.
“Researcher: And like this, 3 weeks later, I return to this tool for this pupil and say to myself: oh right, 3 weeks ago, I had set up this, and well, now the pupil masters it.
Carol: And that makes the teacher to go that far in his reflection and to say to himself, ah yes, I’ve observed that, because sometimes we observe things.
Sophia: And we stop there […]
Lucy: Yes, absolutely. Often due to a lack of time, so we overlook
Carol: While it’s essential to get to that point” (FG 2).
3.2.1.2. Accepted area
Having a list of the essential steps a pupil has to go through to learn to read is considered useful, primarily for novice teachers. Indeed, the tools summarize the essential steps in learning to read. From prerequisites such as phonological awareness to decoding and comprehension of sentences and texts, these tools allow participating teachers to have clear criteria to assess their pupils’ level of proficiency. As Carol explains, “Here, I have the feeling that it is written in a very concrete way, as the teacher would put it into practice in their class” (FG 4). This aspect is mainly considered useful for novice teachers, as it shows them a way to structure their observations and, like this, evaluate all the relevant criteria. As George states (FG 3): “Well, it allowed us to really have an entire summary and to think about all the aspects…” However, in order to keep the use of the tools for reading components manageable, they do not aim to be comprehensive. Indeed, George explains (FG 4): “There are limits to these tools, it should not be taken as a bible but rather as tools allowing us to test certain aspects that we felt were important in learning to read.”
In addition, the different tools allow participating teachers to accomplish different, varying goals, but whose complementary nature is recognized. On the one hand, the whole-class tool for reading components facilitates creating homogeneous ability groups: pupils with the same difficulties can be easily identified and grouped for differentiated instruction. On the other hand, the rubric for reading components is more precise and can be integrated into a tailored learning path. The variety of tools allows future users to select the tool according to their needs. Lucy explains (FG 3): “I think that […] the two versions can complement each other as you said… Well, yes, why not for struggling pupils keep the individual one […] And as it was said to create ability groups, having the collective one can be interesting too…”
3.2.2. Limiting the workload
3.2.2.1. Shared area
Participating teachers perceive progress monitoring as a time-consuming practice. Indeed, collecting data and analyzing it takes a lot of time, especially if teachers have to assess each pupil individually.
“Lucy: Often, we don’t take the time to analyze them [the errors], that’s the problem.
Carol: Because it would take too much time.
Lucy: Because we don’t have the time” (FG 2).
Therefore, the co-developers set out to build tools that can be used with minimal time and effort. To do so, they opted a format that they considered easy to use (i.e., few columns and in landscape format). The developers also opted to write an appendix (see Supplementary material), which allows some theoretical points to be clarified. However, participating teachers insisted on a concise appendix: it should not exceed a few pages. In addition, by relying on the vocabulary in use, which allows an intuitive understanding of the criteria, their goal was to match teachers’ routines. The criteria were arranged chronologically, according to the usual sequence in which a pupil learns to read, and classified under explicit titles. Furthermore, the criteria were intended to be easily observable. The goal of all these measures was to reduce the time and effort required for adequate use of the tools.
“Lucy: It should not be …
Carol: Time-consuming
Lucy: Yes, that’s it, time-consuming and that we have to, I think about my multi-age classroom here, my 21 pupils, I’m thinking, if I have to call them one by one while ensuring that the others are not lost.
Victoria: That’s it.” (FG 1).
3.2.3. Disputed area
The participating teachers discussed at length balancing workload and perceived usefulness. During this discussion, two groups of teachers emerged. The first group considered using the tools too time-consuming compared to the benefits. They considered the rubrics for reading components to be too precise, as they contain various criteria, thus increasing the time investment. In addition, they said that the tools do not provide enough additional elements compared to their usual practices. As Carol explained: “I have just finished some evaluations and in fact, when I was done, I told myself: I did not use the tool. And I read the tool, I thought, but actually, I just did all the work for the school reports […]. This tool is not actually going to help me.” (FG 3). For these teachers, the progress monitoring tools are similar to the school report. Victoria said (FG 3): “But it’s because for me, presented in an individual way, it’s similar, it’s similar to my skills report, really.” However, if the rubric for reading components is used as a school report, the effort of filling out this rubric must be made for each pupil, which considerably increases the workload. Consequently, they intend to use the tools less frequently, on average four times a year.
The second group disagrees with the goals of the rubrics. First, these teachers perceive the tool to be too precise for a school report, and it contains too many technical terms, which could hamper communication with parents. Secondly, they mainly use the rubrics for reading components for struggling pupils. As Georges says, “I really agree with you [about the workload required] but I did not use it for the children who read easily but […] for the children with difficulties, […] it allowed me to find all the aspects that were still complicated for them.” (FG 3). Thirdly, they plan to assess the progress of their struggling pupils on a regular basis, and more frequently than their peers.
These two profiles differ in the number of years of experience in teaching and their class organization. Indeed, the participating teachers belonging to the first group have more years of experience (18 and 24 years) compared to the second group (7 to 9 years). As Sophia explains (FG 3): “And so, when we saw this tool, we said wow, so good, finally something that will structure our thoughts, our work and everything. And when I hear Carol who has already explained to us a little bit about her way of working and everything, Carol you are someone, it seems to me, […] much more organized. […] Your way of evaluating is well planned, step by step, et cetera. And so, I guess, I can understand that in fact, there are like two types of people.”
3.2.4. Flexibility
3.2.4.1. Shared area
To optimize the acceptability of the tools among the teaching routines, the co-developers decided to provide future users with a large number of options. As Carol summarizes:
“Anyway, if we try to impose something on teachers, they will do as they please. Let’s be honest!
George: That’s true” (FG2).
Consequently, the developers aimed to create flexible tools: the co-developers wanted that the tools could be easily adjusted by the teacher, in line with their pedagogical practices, program, or method for teaching reading. In addition, they did not impose a frequency for using the tools, so it could vary according to the teacher’s preference.
“Sophia: In any case, there are simple and complex [sounds], as a comment the teacher can put, “oi, aw, oo” and “dle, gle”…
Carol: He does as he sees fit!
Sophia: That’s it
Carol: Anyway, you’ll do as you please, no?
Sophia and Lucy: Well, yes.
Sophia: And if the teacher can’t make it his own, he won’t do it, he won’t use it anyways.
Lucy: That’s true” (FG 2).
The “blank” version of the tool is further proof of the pursuit of adjustable tools. This version is very simple and allows teachers to choose the order in which they want to evaluate letter-sound correspondences, in line with their program and teaching routines.
The expertise of users is valued as well. The co-developers consider teachers to be competent to provide relevant interventions to remedy pupils’ difficulties and to hypothesize explanations for their origins. The large boxes in the rubrics for reading components allow teachers to provide comments on their observations. In addition, the developers perceive teachers as sufficiently qualified to decide how to assess pupils’ progress. Thus, the tools can be completed on the basis of formal, informal, individual, and/or group assessments. Indeed, the participating teachers consider that, depending on the criteria, the teachers’ routines, and experience, it was impossible to provide the same formal evaluation sheet for all users.
“Researcher: But how am I going to evaluate this? Do I approach the child and have him read 10 words and do it like that or actually, I see the child all day …
Lucy: I observed […]. I would like to say that it depends on […] [the] skills and there are some that can easily be observed and others. So, if we want to evaluate oral reading fluency at some point, it should be good to assess them individually.
Sophia: To let them come see me, yes that’s it. […]
Carol: Yes, but […] in first grade, you make them read every day or you try. You can quickly,
Lucy: Yes, you can quickly complete it, that’s true. (FG 2).
Therefore, filled-out tools cannot be compared between pupils, especially if they are in different classes. For the developers, the main goal is to help teachers monitor their pupils’ progress in reading in first grade. Hence, inter-individual or inter-class comparisons are considered less relevant.
In addition, the tools can be adjusted to the pupils, their level, and their needs. The teacher is not required to complete all of the criteria to make decisions on adapting their teaching practices. Depending on the time of year or the pupils’ abilities, some items may be unnecessary and redundant.
“Carol: There are pupils who need to
Victoria: to go through the intermediate phase.
Carol: That’s right, but it doesn’t concern all of them. Well, me …
Victoria: Let’s say that it can be an extra criterion.
Carol: But again, you can put it as a special criterion. Afterward, it’s up to the teachers to see if they complete this criterion or not” (FG 3).
Therefore, the length of the tools and the resulting workload vary depending on the pupils and the contents already taught.
3.2.4.2. Accepted area
The participants discussed the format of the evaluation as well. Some of the developers argue for a grade, which they believe to be more accurate and objective. Others feel that quantitative assessments are just as subjective as qualitative ratings, but if marks were communicated to pupils, the class atmosphere could be hampered. All criticize a fixed threshold of “50%” for success. On the one hand, as Carol explains: “You cannot read when you can read one word out of two” (FG 2). According to the participating teachers, it is necessary to keep helping the pupils until they master the targeted skill, which corresponds to a grade well above 50%. On the other hand, they note that a fixed threshold of 50%, based on a single assessment, entails serious risks: a pupil with 51% would not receive the same support as a pupil with 49%, even though they both need it.
For these reasons, the co-developers agreed on leaving the format of the evaluation to the teachers, but recommending the categories “Acquired – Not Acquired.” This ensures a usable format for everyone, regardless of whether the completion of the tool was the result of a classroom observation or a formal assessment. Some teachers, like Sophia, argue for the addition of an “In the process of acquiring” category. She was also more comfortable with the use of a more precise percentage, to help her quantify how little or how much a skill was acquired.
“For me, a pupil where it’s ‘not acquired’ but it’s not acquired at 45%, there’s only a small step, but not acquired where we see that we’re in the 10-15%, the step is going to be huge, there will be more work to do” (FG 2).
However, others argued that the addition of another category makes the boundaries between each category more subjective. In addition, since reading is intensively trained in first grade, a pupil’s progress is seen in the number of criteria or letter-sound correspondence acquired over time rather than within each criterion. Thus, they feel that the use of a third category or a percentage were more confusing than helping and that the precision was unnecessary. Despite different practices, the participating teachers agreed on a flexible categorization with the possibility of adding comments. In addition, it allows teachers to distinguish two groups easily: pupils who have mastered the given skill and those who need additional support.
4. Discussion
Progress monitoring has been highlighted as a fruitful teaching practice to reduce the reading achievement gap (Dietrichson et al., 2017; Klute et al., 2017). Yet, progress monitoring is only rarely used in practice, as teachers find it cumbersome to implement (Castro-Villarreal et al., 2014; Cowan and Maxwell, 2015). To create tools suitable for practice, the present study relied on practice-embedded research, based on an iterative and participatory process involving five teachers. This resulted in four tools to monitor pupils’ progress in learning to read at the start of primary education.
Content analyses of the discussions between the developers using an interactionist framework (Morrissette, 2011b; Morrissette and Guignon, 2016) revealed three shared areas of knowledge: perceived usefulness, flexibility, and limiting the workload. At first sight, these needs closely resemble the dimensions as put forward in the Continuous Use Design (Renaud, 2020): usefulness, usability, and acceptability. Indeed, the first dimension includes the relevance of the objectives of the devices, which is similar to the perceived usefulness of our results. The second dimension, usability, can be linked to developers’ desire to limit the workload and optimize the flexibility of the tools, particularly in relation to the target group of pupils. Finally, acceptability in the continuous use design focuses on the compatibility between the tool and the characteristics of the teacher, such as their values and pedagogical style. However, according to the developers in the present study, this acceptability depends on the tools’ flexibility: to guarantee the integration of a tool into the teaching habits, it needs to be easily adjustable to teachers’ practice and to pupils’ level and needs. The results of the present study also further refine the acceptability dimension as put forward by Renaud (2020): rather than considering the three dimensions separately, the tendency to use the tools was found to depend on the balance between the perceived usefulness on the one hand and the workload that a tool requires on the other.
In line with the need for perceived usefulness, the developers agreed that the tools allowed them to identify pupils’ level differences, log information, and differentiate according to pupils’ needs. These findings resemble the key ideas of the Response to Intervention model: the tools allow them to identify those who are floundering and offer them additional support, which corresponds to Tier 2 of the model (Alahmari, 2019). In addition, in line with recommendations (Arden et al., 2017; Filderman and Toste, 2018), some of the teachers used the tool as a more detailed and regular follow-up for struggling pupils. Furthermore, it allows teachers to put pupils with the same difficulty together in homogeneous ability groups (Puzio et al., 2020). Yet, if these groups persist over time, the effect of this form of differentiation can be disadvantageous for struggling pupils (Deunk et al., 2018), possibly even increasing the achievement gap. This is in line with Denessen (2017) on the risk of a possible divergent effect of differentiation as teachers may offer fewer learning opportunities to struggling pupils.
Yet, still in light of the perceived usefulness, developers accepted that the tools summarize essential steps for learning to read and that different tools may serve different goals. These goals are similar to those of the progress monitoring literature: the tools constructed allow teachers to facilitate data collection on pupils’ mastery levels, to provide feedback to pupils, and to translate this information into actions targeting struggling pupils, in the form of differentiation. These steps are also (in part) identified in the literature on progress monitoring (Dietrichson et al., 2017), formative assessment (Klute et al., 2017), and data-driven decision making (Filderman et al., 2018).
The developers also shared a clear desire to limit the workload, as they perceived progress monitoring as cumbersome and time-consuming. These perceptions are consistent with the literature on teachers’ attitudes to data-driven decision making (Schelling and Rubenstein, 2021) and the Response to Intervention model (Greenfield et al., 2010; Cowan and Maxwell, 2015). To this end, the developers ensured that the format of the tools facilitated easy use. However, contrary to what teachers in the context of the USA have suggested (Castro-Villarreal et al., 2014; Schelling and Rubenstein, 2021), the help of colleagues, as well as the prospect of additional resources provided by the school, were not mentioned as strategies to decrease teacher workload. Possibly, this is linked to the lower general level of teacher collaboration: in the French-speaking part of Belgium, teacher collaboration was found to be below the OECD mean (Quittre et al., 2021).
Despite a common desire to reduce the workload and optimize perceived usefulness, two groups emerged when developers balanced both needs. The first group of teachers saw the rubrics for reading components as a report card, necessary for all pupils. The second group used this tool primarily for struggling pupils. The developers identified two characteristics in teachers that set both groups apart: the degree to which a teacher is well-organized and years of seniority. Well-organized and more experienced teachers tended to belong to the first profile. It is possible that their positions are also influenced by their conceptions of justice. Indeed, van Vijfeijken et al. (2021) examined teachers’ arguments to justify their differentiation practices and classified them using the principles of distributive justice: equality (i.e., an equal distribution of resources and/or the same expectations for all learners), equity (i.e., a distribution of resources proportional to their merit such as effort by the learner) and needs (i.e., an unequal distribution of resources based on learners’ needs). The research by van Vijfeijken found that these principles of justice were linked to teachers’ practices of differentiation. Thus, it is possible that, in the present study, the developers who wish to devote more time and effort to struggling pupils (group 2) justify – unconsciously or not – their practices with principles based on learners’ needs, and that the teachers in the first profile place more emphasis on equality. Hence, future research on teachers’ use of progress monitoring tools could specifically examine these principles of distributive justice.
The developers also agreed on the need for flexibility. Consequently, the tools are easily adjustable to pupils’ levels and needs and to teachers’ preferences for monitoring progress. This corroborates the finding by Schelling and Rubenstein (2021) that teachers generally prefer to use their own assessments over standardized tests. Moreover, Van der Kleij et al. (2015) found that foster teachers’ sense of autonomy is linked to a successful implementation of Data-based decision making. However, as pointed out by the co-developers, this has the consequence of limiting comparisons between teachers.
Furthermore, although the developers’ initial goal was to reveal pupils’ skill level differences, one may wonder whether this flexibility may – unintentionally – provide more room for these biases to impact teachers’ judgment. Indeed, teachers tend to have lower expectations for students from disadvantaged backgrounds (for a review, see Wang et al., 2018) and multiple studies have found teacher bias in assessments regarding pupils’ background (Hanna and Linden, 2009; Sprietsma, 2013; von Hippel and Cañedo, 2022). A recent literature review has shown that teachers’ implicit biases sometimes predict their behavior better than their explicit attitudes (Denessen et al., 2022). For example, Gortazar et al. (2022) conducted a large study comparing the grades awarded to an assessment by two raters: an external assessor and pupils’ primary school teachers. For languages (Basque and Spanish), results indicate that boys, first and second-generation immigrants, and pupils from disadvantaged backgrounds are judged more negatively by their teacher than by the external assessor. These biases may play a stronger role in teachers’ judgment when tools are flexible. Indeed, the study by Quinn (2020) found that, when using a detailed rubric (implying a low level of flexibility), this led to a fairer judgment of the skill level of ethnic minority pupils. In other words, the flexibility could lead to a disadvantageous assessment of pupils from disadvantaged backgrounds and, when not combined with differentiated support, increase educational inequalities.
Developers agreed that the practice field is complex and diverse, as previously highlighted in the literature on practice-embedded research (Snow, 2015; Goigoux et al., 2021). Consequently, diverging views were considered inevitable and the need to value teacher expertise was underlined. In this way, the complexity of the field was handled through the flexibility of the tools. This flexibility extends the conditions under which the tools can be implemented, which is advocated in practice-embedded research (Class and Schneider, 2013).
In light of the need for flexibility, developers have deliberately allowed teachers liberty in the format of the evaluation (accepted area of knowledge). This implies that a very wide range of information sources can be considered, such as formal assessments and classroom observations, both for one specific, struggling pupil (tool 1) or for the entire classroom (tools 2 and 3). Within these tools, qualitative and quantitative data are considered equal sources of information. This position is contrary to the literature on data-based decision making, which advocates quantitative, even standardized data (Filderman et al., 2018), but is closer to formative evaluation, which defines the term ‘data’ more broadly (Allal and Mottier Lopez, 2005; Eysink and Schildkamp, 2021).
4.1. Limitations and implications for future research
Some limitations of the present study need to be acknowledged. First, the tools were constructed by a small group of volunteer teachers, who all pursued or are currently pursuing additional qualifications such as a Master’s in educational sciences. In addition, the views of the teachers evolved over the course of the different focus groups. Although the developers attempted to create the most flexible tools for any type of primary education context, due to the limited sample and the impact of the joint creation process, future research should examine whether teachers without master level training and who did not participate in the focus group discussions can easily use them. There is early, anecdotal evidence that this is possible: two teachers gave the tools to a colleague, who found them useful. It is clear that more experiences from teachers who did not participate in the development are welcome, as these may further refine the tools (Cèbe and Goigoux, 2018). This may also point to other key teacher characteristics, besides seniority and the degree to which one is organized as detected in the present study.
Second, it needs to be emphasized that the tools were developed in the context of learning to read French at the start of primary education. Reading is a complex skill and multiple components interact when learning to read (Scarborough, 2005; Peters et al., 2022). This complexity prompted the developers to create flexible progress monitoring tools. It remains to be investigated whether progress monitoring tools for reading in the later years of primary education or for other key content domains (e.g., mathematics) require the same level of flexibility.
Third, although progress monitoring and, more broadly, formative assessment are believed to foster pupils’ achievement (Dietrichson et al., 2017; Klute et al., 2017), the present study did not set out to examine whether the co-created tools live up to this claim. Further research is needed on whether progress monitoring using these tools positively impacts pupils’ reading achievement and if the tools differ in this respect. For researchers examining educational inequalities, this is also timely as all previous studies on reading combined progress monitoring with other teaching practices aimed at reducing the achievement gap (Dietrichson et al., 2017, 2021). Hence, the precise effect of progress monitoring in itself remains unclear. To design adequate interventions combining multiple teacher practices to reduce the achievement gap, it is first important to gain an insight into the effectiveness of each teacher practice separately.
Finally, it is worth emphasizing that while progress monitoring may have a positive effect, it is unlikely that this practice reduces the achievement gap to an acceptably low level. Rather, it is likely to be a necessary first step in identifying struggling pupils and providing adequate interventions for these pupils. While the co-developers in the present study were confident of their own ability as teachers and that of their colleagues to provide relevant interventions, this merits further research as well.
4.2. Implications for practice
The present research expands on the previous literature with regard to teaching practices targeting a decrease in educational inequalities and the literature on progress monitoring more specifically. Rather than a researcher-led development of progress monitoring tools, the present study relied on a practice-embedded research: teachers and researchers co-created tools to help monitor pupils’ progress in reading. This resulted in four tools (see Supplementary material) that practitioners can use. In addition, the tools may become part of the resources provided during teacher training.
Moreover, the content analysis of the focus group discussions revealed an important topic for future professional development. The developers discussed at length balancing workload and perceived usefulness. If schools want to put progress monitoring in place, it is likely that a disputed area of knowledge would cause disagreement among teachers. Hence, professional development in school teams could anticipate and ensure that teachers can express their views and that a consensus can be reached on this topic.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.
Author contributions
EF was in charge of organizing and conducting the focus groups, analyzing the data, and writing the first draft of the manuscript. SC was actively involved in recruiting the participants and contributed to the revision of the manuscript, mainly on methodological aspects. LC and SC contribuated equally and share last authorship and equally supervised the data collection and analyses. LC contributed to revising the manuscript, mostly on the introduction and discussion. All authors conceptualized the research project, defined the research question, developed the research design and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2023.1111420/full#supplementary-material
Footnotes
1. ^The reference researcher is the first author of the article.
2. ^The verbatims were translated from French.
References
Adesope, O. O., Trevisan, D. A., and Sundararajan, N. (2017). Rethinking the use of tests: a meta-analysis of practice testing. Rev. Educ. Res. 87, 659–701. doi: 10.3102/0034654316689306
Al Otaiba, S., Baker, K., Lan, P., Allor, J., Rivas, B., Yovanoff, P., et al. (2019). Elementary teacher’s knowledge of response to intervention implementation: a preliminary factor analysis. Ann. Dyslexia 69, 34–53. doi: 10.1007/s11881-018-00171-5
Alahmari, A. (2019). A review and synthesis of the response to intervention (RtI) literature: teachers implementations and perceptions. J. Educ. Pract. 10:8. doi: 10.7176/JEP/10-15-02
Allal, L., and Mottier Lopez, L. (2005) Formative Assessment: Improving Learning in Secondary Classrooms. Edited by Centre for Educational Research and Innovation. Paris: OECD.
Arden, S. V., Gandhi, A. G., Zumeta Edmonds, R., and Danielson, L. (2017). Toward more effective tiered systems: lessons from National Implementation Efforts. Except. Child. 83, 269–280. doi: 10.1177/0014402917693565
Ardoin, S. P., Christ, T. J., Morena, L. S., Cormier, D. C., and Klingbeil, D. A. (2013). A systematic review and summarization of the recommendations and research surrounding curriculum-based measurement of oral reading fluency (CBM-R) decision rules. J. Sch. Psychol. 51, 1–18. doi: 10.1016/j.jsp.2012.09.004
Balu, R., Zhu, P., Doolittle, F., Schiller, E., and Gersten, R. (2015). Evaluation of Response to Intervention Practices for Elementary School Reading. NCEE-2016-4000. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
Baribeau, C. (2009). Analyse des données des entretiens de groupe. Recherches Qual. 28:133. doi: 10.7202/1085324ar
Berkeley, S., Scanlon, D., Bailey, T. R., Sutton, J. C., and Sacco, D. M. (2020). A snapshot of RTI implementation a decade later: new picture, same story. J. Learn. Disabil. 53, 332–342. doi: 10.1177/0022219420915867
Bianco, M. (2018). La réponse à des questions cruciales en éducation réside-t-elle dans un changement de paradigme? Éducation et didactique 12–1, 121–128. doi: 10.4000/educationdidactique.3111
Bianco, M., Pellenq, C., Lambert, E., Bressoux, P., Lima, L., and Doyen, A. L. (2012). Impact of early code-skill and oral-comprehension training on reading achievement in first grade. J. Res. Read. 35, 427–455. doi: 10.1111/j.1467-9817.2010.01479.x
Billard, C., Lequette, C., Pouget, G., Pourchet, M., and Zorman, M. (2013). OURA LEC/CP Outil enseignant. Available at: http://www.cognisciences.com/accueil/outils/article/oura-lec-cp-outil-enseignant Last accessed date: 25/11/2022.
Black, P., and Wiliam, D. (2009). Developing the theory of formative assessment, educational assessment, evaluation and accountability. J. Pers. Eval. Educ. 21, 5–31. doi: 10.1007/s11092-008-9068-5
Bogaerds-Hazenberg, S. T. M., Evers-Vermeul, J., and van den Bergh, H. (2019). Teachers and researchers as co-designers? A design-based research on reading comprehension instruction in primary education. EDeR 3, 1–24. doi: 10.15460/eder.3.1.1399
Bondie, R. S., Dahnke, C., and Zusho, A. (2019). How does changing “one-size-fits-all” to differentiated instruction affect teaching? Rev. Res. Educ. 43, 336–362. doi: 10.3102/0091732X18821130
Bressoux, P. (2021). “A quelles conditions peut-on déployer à grande échelle les interventions qui visent à améliorer les pratiques enseignantes?” in Améliorer les pratiques en éducation Qu’en dit la recherche? eds. B. Galand and M. Janosz (Louvain-La-Neuve: Presses universitaires de Louvain).
Bricteux, S., and Quittre, V. (2021) Résultats de PISA 2018 en Fédération Wallonie-Bruxelles Des différences aux inégalités. Service d’Analyse des Systèmes et des Pratiques d’enseignement.
Bryk, A. S. (2015). Accelerating how we learn to improve. Educ. Res. 44, 467–477. doi: 10.3102/0013189X15621543
Bryk, A. S. (2017). Accélérer la manière dont nous apprenons à améliorer. Éducation et didactique 11, 11–29. doi: 10.4000/educationdidactique.2796
Caena, F. (2011) Teachers’ Continuing Professional Development. Brussels: European Commission, p. 21.
Calkins, L. (2017) A guide to the Reading workshop: Primary grades. Teachers College Reading and Writing Project. New York, NY: Columbia University.
Castro-Villarreal, F., Rodriguez, B. J., and Moore, S. (2014). Teachers’ perceptions and attitudes about response to intervention (RTI) in their schools: a qualitative analysis. Teach. Teach. Educ. 40, 104–112. doi: 10.1016/j.tate.2014.02.004
Cèbe, S., and Goigoux, R. (2018). Lutter contre les inégalités: outiller pour former les enseignants. Recherche Format. 87, 77–96. doi: 10.4000/rechercheformation.3510
Christ, T. J., Zopluoglu, C., Long, J. D., and Monaghen, B. D. (2012). Curriculum-based measurement of oral reading: quality of progress monitoring outcomes. Except. Child. 78, 356–373. doi: 10.1177/001440291207800306
Class, B., and Schneider, D. (2013). La Recherche Design en Education: vers une nouvelle approche? Frantice.net 7:5.
Colognesi, S., and Gouin, J.-A. (2022). A typology of learner profiles to anticipate and guide differentiation in primary classes. Res. Pap. Educ. 37, 479–495. doi: 10.1080/02671522.2020.1849376
Coppe, T., März, V., Decuypere, M., Springuel, F., and Colognesi, S. (2018). Ouvrir la boîte noire du travail de préparation de l’enseignant: essai de modélisation et d’illustration autour du choix et de l’évolution d’un document support de cours. Revue française de pédagogie. Recherches en éducation 204, 17–31. doi: 10.4000/rfp.8358
Cowan, C., and Maxwell, G. (2015) ‘Educators’ perceptions of response to intervention implementation and impact on student learning’, Journal of Instructional Pedagogies. Available at: https://eric.ed.gov/?id=EJ1069392 (Accessed July 8, 2022)
Davis-Kean, P. E. (2005). The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment. J. Fam. Psychol. 19, 294–304. doi: 10.1037/0893-3200.19.2.294
Denessen, E. (2017) ‘Dealing Responsibly with Differences. Socio-Cultural Backgrounds and Differentiation in Education ’. Leiden: Universiteit Leiden.
Denessen, E., Hornstra, L., van den Bergh, L., and Bijlstra, G. (2022). Implicit measures of teachers’ attitudes and stereotypes, and their effects on teacher practice and student outcomes: a review. Learn. Instr. 78:101437. doi: 10.1016/j.learninstruc.2020.101437
Deum, M., Gabelica, C., Lafontaine, A., Nyssen, M.-C., and Lafontaine, D. (2007) ‘Outil pour le diagnostic et la remédiation des difficultés d’acquisition de la lecture en 1re et 2e années primaires’. Service général du Pilotage du système éducatif.
Deunk, M. I., Smale-Jacobse, A. E., de Boer, H., Doolaard, S., and Bosker, R. J. (2018). Effective differentiation practices: a systematic review and meta-analysis of studies on the cognitive effects of differentiation practices in primary education. Educ. Res. Rev. 24, 31–54. doi: 10.1016/j.edurev.2018.02.002
Dietrichson, J., Bøg, M., Filges, T., and Klint Jørgensen, A. M. (2017). Academic interventions for elementary and middle school students with low socioeconomic status: a systematic review and Meta-analysis. Rev. Educ. Res. 87, 243–282. doi: 10.3102/0034654316687036
Dietrichson, J., Filges, T., Seerup, J. K., Klokker, R. H., Viinholt, B. C. A., Bøg, M., et al. (2021). Targeted school-based interventions for improving reading and mathematics for students with or at risk of academic difficulties in grades K-6: a systematic review. Campbell Syst. Rev. 17:1152. doi: 10.1002/cl2.1152
Donovan, M. S., Snow, C., and Daro, P. (2013). The SERP approach to problem-solving research, development, and implementation. Yearbook Natl. Soc. Study Educ. 115, 400–425. doi: 10.1177/016146811311501411
Dupriez, V. (1999). “La liberté pédagogique comme condition de la concurrence” in Le décret du 24 juillet 1997 définissant les missions prioritaires de l’enseignement. eds. H. Dumon and M. Collin (Bruxelles, Belgique: Approche interdisciplinaire, Presses des FUSL), 211–222.
Eysink, T. H. S., and Schildkamp, K. (2021). A conceptual framework for assessment-informed differentiation (AID) in the classroom. Educ. Res. 63, 261–278. doi: 10.1080/00131881.2021.1942118
Fédération Wallonie-Bruxelles. (2022). Les indicateurs de l’enseignement. Administration générale de l’Enseignement. 17e édition.
Filderman, M. J., and Toste, J. R. (2018). Decisions, decisions, decisions: using data to make instructional decisions for struggling readers. Teach. Except. Child. 50, 130–140. doi: 10.1177/0040059917740701
Filderman, M. J., Toste, J. R., Didion, L. A., Peng, P., and Clemens, N. H. (2018). Data-based decision making in Reading interventions: a synthesis and Meta-analysis of the effects for struggling readers. J. Spec. Educ. 52, 174–187. doi: 10.1177/0022466918790001
Fuchs, D., and Fuchs, L. S. (2017). Critique of the National Evaluation of response to intervention: a case for simpler frameworks. Except. Child. 83, 255–268. doi: 10.1177/0014402917693580
Gaitas, S., and Alves Martins, M. (2017). Teacher perceived difficulty in implementing differentiated instructional strategies in primary school. Int. J. Incl. Educ. 21, 544–556. doi: 10.1080/13603116.2016.1223180
Gennetian, L. A., Castells, N., and Morris, P. A. (2010). Meeting the basic needs of children: does income matter? Child Youth Serv. Rev. 32, 1138–1148. doi: 10.1016/j.childyouth.2010.03.004
Gersten, R., Haymond, K., Newman-Gonchar, R., Dimino, J., and Jayanthi, M. (2020). Meta-analysis of the impact of Reading interventions for students in the primary grades. J. Res. Educ. Effect. 13, 401–427. doi: 10.1080/19345747.2019.1689591
Gersten, R., Jayanthi, M., and Dimino, J. (2017). Too much, too soon? Unanswered questions from National Response to intervention evaluation. Except. Child. 83, 244–254. doi: 10.1177/0014402917692847
Godor, B. P. (2021). The many faces of teacher differentiation: using Q methodology to explore teachers preferences for differentiated instruction. Teach. Educ. 56, 43–60. doi: 10.1080/08878730.2020.1785068
Goigoux, R. (2017). Associer chercheurs et praticiens à la conception d’outils didactiques ou de dispositifs innovants pour améliorer l’enseignement. Éducation et didactique 11, 135–142. doi: 10.4000/educationdidactique.2872
Goigoux, R., Renaud, J., and Roux-Baron, I. (2021) ‘Comment influencer positivement les pratiques pédagogiques de professeurs expérimentés?’, in B. Galand and M. Janosz, Améliorer les pratiques en éducation: Qu’en dit la recherche? Presses universitaires de Louvain, pp. 67–76.
Gortazar, L., Martinez de Lafuente, D., and Vega-Bayo, A. (2022). Comparing teacher and external assessments: are boys, immigrants, and poorer students undergraded? Teach. Teach. Educ. 115:103725. doi: 10.1016/j.tate.2022.103725
Gottheiner, D. M., and Siegel, M. A. (2012). Experienced middle school science teachers’ assessment literacy: investigating knowledge of students’ conceptions in genetics and ways to shape instruction. J. Sci. Teach. Educ. 23, 531–557. doi: 10.1007/s10972-012-9278-z
Greenfield, R., Rinaldi, C., Proctor, C. P., and Cardarelli, A. (2010). Teachers’ perceptions of a response to intervention (RTI) reform effort in an urban elementary school: a consensual qualitative analysis. J. Disabil. Policy Stud. 21, 47–63. doi: 10.1177/1044207310365499
Hanin, V., Colognesi, S., Cambier, A. C., Bury, C., and van Nieuwenhoven, C. (2022). Association between prospective elementary school teachers’ year of study and their type of conception of intelligence. Int. J. Educ. Res. 115:102039. doi: 10.1016/j.ijer.2022.102039
Hanna, R., and Linden, L. (2009) ‘Measuring discrimination in education’. National Bureau of Economic Research (Working Paper Series). doi:10.3386/w15057.
Hargreaves, E. (2013). Inquiring into children’s experiences of teacher feedback: reconceptualising assessment for learning. Oxf. Rev. Educ. 39, 229–246. doi: 10.1080/03054985.2013.787922
Hart, B., and Risley, T. R. (2003). The early catastrophe: the 30 million word gap by age 3. Am. Educ. 27, 4–9.
Hebbecker, K., Förster, N., Forthmann, B., and Souvignier, E. (2022). Data-based decision-making in schools: examining the process and effects of teacher support. J. Educ. Psychol. 114, 1695–1721. doi: 10.1037/edu0000530
Hughes, C. A., and Dexter, D. D. (2011). Response to intervention: a research-based summary. Theory Pract. 50, 4–11. doi: 10.1080/00405841.2011.534909
Klute, M., Apthorp, H., Harlacher, J., and Reale, M. (2017) Formative assessment and elementary school student academic achievement: A review of the evidence. REL 2017–259. National Center For Education Evaluation and Regional Assistance, p. 53. Available at: http://ies.ed.gov/ncee/edlabs. Last accessed date: 19/04/2022.
Le Normand, M.-T., Parisse, C., and Cohen, H. (2008). Lexical diversity and productivity in French preschoolers: developmental, gender and sociocultural factors. Clin. Linguist. Phon. 22, 47–58. doi: 10.1080/02699200701669945
Lemons, C. J., Kearns, D. M., and Davidson, K. A. (2014). Data-based individualization in Reading: intensifying interventions for students with significant Reading disabilities. Teach. Except. Child. 46, 20–29. doi: 10.1177/0040059914522978
Magnuson, K., and Shager, H. (2010). Early education: Progress and promise for children from low-income families. Child Youth Serv. Rev. 32, 1186–1198. doi: 10.1016/j.childyouth.2010.03.006
Morrissette, J. (2011a). ‘Formative assessment: revisiting the territory from the point of view of teachers’, McGill. J. Educ. 46, 247–265. doi: 10.7202/1006438ar
Morrissette, J. (2011b). Vers un cadre d’analyse interactionniste des pratiques professionnelles. Recherches qualitatives 30, 10–32. doi: 10.7202/1085478ar
Morrissette, J., and Guignon, S. (2016). Trois zones de coconstruction de savoirs professionnels issues des médiations de débats en groupe. Communiquer. Revue de communication sociale et publique 18, 117–130. doi: 10.4000/communiquer.2085
Nadeau, A. (2021). Conceptions d’enseignants du primaire sur leur rôle de passeur culturel: effets de dispositifs d’intégration de la dimension culturelle à l’école québécoise. Recherches qualitatives 40, 128–153. doi: 10.7202/1076350ar
Neitzel, A. J., Lake, C., Pellegrini, M., and Slavin, R. E. (2021). A synthesis of quantitative research on programs for struggling readers in elementary schools. Read. Res. Q. 57, 149–179. doi: 10.1002/rrq.379
Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., et al. (2012). Intelligence: new findings and theoretical developments. Am. Psychol. 67, 130–159. doi: 10.1037/a0026699
Oslund, E. L., Elleman, A. M., and Wallace, K. (2021). Factors related to data-based decision-making: examining experience, professional development, and the mediating effect of confidence on teacher graph literacy. J. Learn. Disabil. 54, 243–255. doi: 10.1177/0022219420972187
Peters, M. T., Hebbecker, K., and Souvignier, E. (2022). Effects of providing teachers with tools for implementing assessment-based differentiated Reading instruction in second grade. Assess. Eff. Interv. 47, 157–169. doi: 10.1177/15345084211014926
Prenger, R., and Schildkamp, K. (2018). Data-based decision making for teacher and student learning: a psychological perspective on the role of the teacher. Educ. Psychol. 38, 734–752. doi: 10.1080/01443410.2018.1426834
Puzio, K., Colby, G. T., and Algeo-Nichols, D. (2020). Differentiated literacy instruction: boondoggle or best practice? Rev. Educ. Res. 90, 459–498. doi: 10.3102/0034654320933536
Quinn, D. M. (2020). Experimental Evidence on Teachers’ Racial Bias in Student Evaluation: The Role of Grading Scales. Educational Evaluation and Policy Analysis 42, 375–392. doi: 10.3102/0162373720932188
Quinn, D. M., and Kim, J. S. (2017). Scaffolding Fidelity and adaptation in educational program implementation: experimental evidence from a literacy intervention. Am. Educ. Res. J. 54, 1187–1220. doi: 10.3102/0002831217717692
Quittre, V., Dupont, V., and Lafontaine, D. (2021) ‘Des enseignants parlent aux enseignants Résultats de l’enquête TALIS 2018’. Service d’Analyse des Systèmes et des Pratiques d’enseignement.
Renard, F., Demeuse, M., Castin, J., and Dagnicourt, J. (2022). De la structure légère de pilotage au Pacte pour un Enseignement d’excellence Le glissement progressif d’un pilotage incitatif à un pilotage par les résultats et la reddition de comptes en Belgique francophone. Les Dossiers des Sciences de L Éducation 45, 33–56.
Renaud, J. (2020). Évaluer l’utilisabilité, l’utilité et l’acceptabilité d’un outil didactique au cours du processus de conception continuée dans l’usage. Éducation et didactique 14–2, 65–84. doi: 10.4000/educationdidactique.6756
Roy, A., Guay, F., and Valois, P. (2013). Teaching to address diverse learning needs: development and validation of a differentiated instruction scale. Int. J. Incl. Educ. 17, 1186–1204. doi: 10.1080/13603116.2012.743604
Scarborough, H. S. (2005). “Developmental relationships between language and Reading: reconciling a beautiful hypothesis with some ugly fact” in Catts, H. W., the Connections Between Language and Reading Disabilities. ed. A. G. Kamhi (Mahwah, NJ: Lawrence Erlbaum Associates Publishers), 3–24.
Schelling, N., and Rubenstein, L. D. (2021). Elementary teachers’ perceptions of data-driven decision-making. Educ. Assess. Eval. Account. 33, 317–344. doi: 10.1007/s11092-021-09356-w
Schildkamp, K. (2019). Data-based decision-making for school improvement: research insights and gaps. Educ. Res. 61, 257–273. doi: 10.1080/00131881.2019.1625716
Schillings, P., Dupont, V., Géron, S., and Matoul, A. (2017) PIRLS 2016: Note de synthèse, p. 22. Available at: http://hdl.handle.net/2268/216693. Last accessed date: 26/12/2021.
Slates, S. L., Alexander, K. L., Entwisle, D. R., and Olson, L. S. (2012). Counteracting summer slide: social capital resources within socioeconomically disadvantaged families. J. Educ. Stud. Placed Risk 17, 165–185. doi: 10.1080/10824669.2012.688171
Slavin, R. E., Lake, C., Davis, S., and Madden, N. A. (2011). Effective programs for struggling readers: a best-evidence synthesis. Educ. Res. Rev. 6, 1–26. doi: 10.1016/j.edurev.2010.07.002
Snow, C. E. (2015). 2014 Wallace Foundation distinguished lecture: rigor and realism: doing educational science in the real world. Educ. Res. 44, 460–466. doi: 10.3102/0013189X15619166
Sprietsma, M. (2013). Discrimination in grading: experimental evidence from primary school teachers. Empir. Econ. 45, 523–538. doi: 10.1007/s00181-012-0609-x
Stecker, P. M., Lembke, E. S., and Foegen, A. (2008). Using Progress-monitoring data to improve instructional decision making. Prevent. School Failure Alternat. Educ. Child. Youth 52, 48–58. doi: 10.3200/PSFL.52.2.48-58
Taylor, B., Hodgen, J., Tereshchenko, A., and Gutiérrez, G. (2022). Attainment grouping in English secondary schools: a national survey of current practices. Res. Pap. Educ. 37, 199–220. doi: 10.1080/02671522.2020.1836517
The Design-Based Research Collective (2003). Design-based research: an emerging paradigm for educational inquiry. Educ. Res. 32, 5–8. doi: 10.3102/0013189X032001005
UNICEF Office of Research (2016) Fairness for Children: A League Table of Inequality in Child Well-Being in Rich Countries. Florence: UNICEF Office of Research - Innocenti (Innocenti Report Card 13).
van der Kleij, F. M., Vermeulen, J. A., Schildkamp, K., and Eggen, T. J. H. M. (2015). Integrating data-based decision making, assessment for learning and diagnostic testing in formative assessment. Assess. Educ. 22, 324–343. doi: 10.1080/0969594X.2014.999024
van Geel, M., Keuning, T., Frèrejean, J., Dolmans, D., van Merriënboer, J., and Visscher, A. J. (2019). Capturing the complexity of differentiated instruction. Sch. Eff. Sch. Improv. 30, 51–67. doi: 10.1080/09243453.2018.1539013
Van Nieuwenhoven, C., and Colognesi, S. (2015). Une recherche collaborative sur l’accompagnement des futurs instituteurs: un levier de développement professionnel pour les maîtres de stage. e-Jiref 1, 103–121.
Van Norman, E. R., Nelson, P. M., and Parker, D. C. (2018). A comparison of nonsense-word fluency and curriculum-based measurement of reading to measure response to phonics instruction. Sch. Psychol. Q. 33, 573–581. doi: 10.1037/spq0000237
van Vijfeijken, M., Denessen, E., Schilt-Mol, T. V., and Scholte, R. H. J. (2021). ‘Equity, equality, and need: a qualitative study into teachers’ professional trade-offs in justifying their differentiation practice. Open J. Soc. Sci. 9, 236–257. doi: 10.4236/jss.2021.98017
Visscher, A. J. (2021). On the value of data-based decision making in education: the evidence from six intervention studies. Stud. Educ. Eval. 69:100899. doi: 10.1016/j.stueduc.2020.100899
von Hippel, P. T., and Cañedo, A. P. (2022). Is kindergarten ability group placement biased? New data, new methods, new answers. Am. Educ. Res. J. 59, 820–857. doi: 10.3102/00028312211061410
Wang, S., Rubie-Davies, C. M., and Meissel, K. (2018). A systematic review of the teacher expectation literature over the past 30 years. Educ. Res. Eval. 24, 124–179. doi: 10.1080/13803611.2018.1548798
Wayman, J. C., Jimerson, J. B., and Cho, V. (2012). Organizational considerations in establishing the data-Informed District. Sch. Eff. Sch. Improv. 23, 159–178. doi: 10.1080/09243453.2011.652124
Yang, C., Luo, L., Vadillo, M. A., Yu, R., and Shanks, D. R. (2021). Testing (quizzing) boosts classroom learning: a systematic and meta-analytic review. Psychol. Bull. 147, 399–435. doi: 10.1037/bul0000309
Yin, Y., Tomita, M. K., and Shavelson, R. J. (2014). Using formal embedded formative assessments aligned with a short-term learning progression to promote conceptual change and achievement in science. Int. J. Sci. Educ. 36, 531–552. doi: 10.1080/09500693.2013.787556
Keywords: progress monitoring, formative assessment, practice-embedded research, teaching practices, reading
Citation: Francotte E, Colognesi S and Coertjens L (2023) Co-creating tools to monitor first graders’ progress in reading: a balancing act between perceived usefulness, flexibility, and workload. Front. Educ. 8:1111420. doi: 10.3389/feduc.2023.1111420
Edited by:
Philipp Sonnleitner, University of Luxembourg, LuxembourgReviewed by:
Jens Dietrichson, Danish Center for Social Science Research (VIVE), DenmarkFernando Morales Villabona, Haute École Pédagogique du Canton de Vaud, Switzerland
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*Correspondence: Eve Francotte, ZXZlLmZyYW5jb3R0ZUB1Y2xvdXZhaW4uYmU=
†These authors have contributed equally to this work and share last authorship