Tova Michalsky- Bar-Ilan University, Ramat Gan, Israel
Noting the important role of motivation in science students' reading comprehension, this 14-weeks quasi-experiment investigated the optimal timing for implementation of metamotivational scaffolding for self-regulation of scientific text comprehension. The “IMPROVE” metamotivational self-regulatory model (Introducing new concepts, Metamotivation questioning, Practicing, Reviewing and reducing difficulties, Obtaining mastery, Verification, and Enrichment) was embedded at three different phases of secondary students' engagement with scientific texts and exercises (before, during, or after) to examine effects of timing on groups' science literacy and motivational regulation. Israeli 10th graders (N = 202) in eight science classrooms received the same scientific texts and reading comprehension exercises in four groups. Three treatment groups received metamotivational scaffolding before (n = 52), during (n = 50), or after text engagement (n = 54). The control group (n = 46) received standard instructional methods with no metamotivational scaffolding. Pretests and posttests assessed science literacy, domain-specific microbiology knowledge, and metamotivation regulation. Intergroup differences were non-significant at pretest but significant at posttest. The “before” group significantly outperformed all other groups. The “after” group significantly outperformed the “during” group, and the control group scored lowest. Outcomes suggested delivery of metamotivational scaffolding as a potentially important means for promoting students' science literacy and effortful perseverance with challenging science tasks, especially at the reflection-before-action stage for looking ahead and also at the reflection-on-action stage for looking back. More theoretical and practical implications of this preliminary study were discussed to meet the growing challenges in science teaching schoolwork.
Introduction
Reading comprehension of scientific texts is a well-recognized, powerful vehicle for engaging students' minds and helping them construct scientific inquiry habits, reach a deep conceptual understanding, and attain science achievements (Graesser et al., 2002; Krajcik and Sutherland, 2010; Pearson et al., 2010; Yore and Tippett, 2014; van Rijk et al., 2017; Sason et al., 2020). However, researchers have asserted that the mere provision of reading opportunities and strategies is often insufficient to effectively develop science literacy without explicit scaffolding to support readers' self-regulated learning (Lai et al., 2014; Murphy et al., 2017). The Programme for International Student Assessment (PISA) defines science literacy as “the ability to engage with science-related issues, and with the ideas of science as a reflective citizen” (Organisation for Economic Co-operation Development, 2015a, p. 50).
Motivation plays an important role in science students' reading by determining the extent to which students engage with science texts and persevere in applying effort, without aborting, until successfully completing the texts' accompanying reading comprehension tasks (see review by Morgan and Fuchs, 2007). Thus, when facing a reading task, students must not only attain knowledge about reading comprehension strategies such as locating ideas in text and processing and integrating information—namely, cognitive self-regulation skills—but also must attain knowledge about how and when to apply these different cognitive strategies—namely, metacognitive self-regulation skills (e.g., Schreiber, 2005; Roebers, 2017; Jian, 2018; Pamungkas et al., 2018; Farhana et al., 2020). Students must also acquire explicit strategies for self-regulating their own motivation—deciding how to approach the knowledge acquisition process and how much effort to invest—to cope with what may be a cognitively, emotionally, and temporally demanding task (e.g., McClelland et al., 2007; Guthrie and Coddington, 2009; Kelley and Decker, 2009; Logan et al., 2011; Skibbe et al., 2011; Liew et al., 2019; Li et al., 2020).
Most prior research on learners' self-regulation in the reading context has focused on supporting the cognitive and metacognitive aspects of self-regulated learning (also see Ahmadi et al., 2013; Rastegar et al., 2017; see reviews in Ali and Razali, 2019 and Deliany and Cahyono, 2020). In contrast, the motivational aspect has been under-investigated (McNamara, 2017; Egloff, 2019; Egloff and Souvignier, 2020). Embedment of self-questions into learning material to guide students' autonomous or collaborative self-regulation of their own learning processes (e.g., Kramarski and Mevarech, 1997 “IMPROVE” method) has been shown effective for enhancing science and math learners' cognitive and metacognitive self-regulation as well as for promoting their academic achievements (Michalsky, 2013, 2020; Michalsky and Schechter, 2018). However, most prior research on self-questioning supports has been conducted in the context of cognitive and metacognitive self-regulation components rather than the motivational self-regulation component.
At the focus of the current study, previous research has not yet sufficiently explored when best to specifically embed motivational scaffolding to maximize science learners' active engagement in, and comprehension of, reading tasks. Although scaffolding to support self-regulated learning processes may be implemented at different chronological phases of learning—at the forethought, performance, or retrospective self-reflection phases (Zimmerman, 1990)—little attention has been given in the empirical literature to the relative effectiveness of the before-task vs. during-task vs. after-task timeframe for delivering motivational regulation support for scientific text reading. The current study aims to narrow this gap by expanding the literature on the benefit of scaffolding for the motivational aspect of self-regulated learning in a science reading context and, in particular, on the design of its optimal conditions.
Metamotivation Processes for Scientific Text Reading
Motivational self-regulation, otherwise termed “metamotivation,” refers to the conscious processing of monitoring and controlling one's own motivation to increase effort and persistence when completing a task or achieving a particular learning goal (McNamara, 2017; Schwinger and Otterpohl, 2017). Specifically, motivational regulation includes two main component processes: monitoring and control (Corno, 1993; Boekaerts, 1995; Kuhl, 2000; Wolters, 2003, 2011; Pintrich, 2004; Sansone and Thoman, 2005, 2006; Schwinger and Stiensmeier-Pelster, 2012). Metamotivational “monitoring” refers to self-awareness or self-evaluation of one's motivation, whereas metamotivational “control” refers to self-management and self-regulation of one's motivation and efforts (Zeidner and Stoeger, 2019), operating as reciprocal processes that form a feedback loop (Miele and Scholer, 2018).
Metamotivational monitoring refers to students' evaluation of the quantity and the intrinsic or extrinsic quality of their own motivation to achieve a goal or complete a task. In the context of science text comprehension, students must be motivated to invest effort in trying to understand the written material and in monitoring their comprehension to observe lapses in understanding (Oakhill and Cain, 2012; Dutke et al., 2016; De Smedt et al., 2020). Such monitoring includes recognizing what motivated one to read about a science topic in the first place, what decreases one's motivation, how one's current motivation can be informed by motivation in prior similar tasks, and what one still needs to monitor so as to remediate discrepancies in one's motivation to read. According to Meniado (2016), the development of science reading comprehension skills is significantly better for learners who monitor their reading motivation.
Metamotivational control refers to choosing and actively performing strategies that strengthen or shift one's motivation. Such strategies include the use of self-talk to regulate efforts and actions, environmental control efforts to establish external conditions that are more conducive to learning effectively, and “self-consequating” behaviors, where students promise themselves a reward or reinforcement after achieving their academic goal (Schwinger and Otterpohl, 2017). Students who manage their motivation while reading—by taking a more active interest in the topic, finding a personal connection to the material, trying to reduce outside distractions, and mobilizing attention to decipher difficult ideas presented in the text—have reported experiencing a more successful learning process, gaining a sense of satisfaction and enjoyment from learning, and understanding the study topics better (Kamil et al., 2008; Salinger, 2010; Schwinger and Otterpohl, 2017).
In the context of comprehending written scientific content, metamotivational monitoring can encourage students to think about (i.e., to self-evaluate) what motivates them to comprehend a text. Having identified their reasons, readers can then take actions to self-manage (i.e., control) their engagement with the task, with the aim of increasing their motivation and, in turn, their reading comprehension (Bråten et al., 2013). For example, Azhari (2020) has recently reported that monitoring of reading motivation is a driving factor that encourages students to manage and meet their expressed goals. The experiments of Nguyen et al. (2019) have also demonstrated that, to manage motivational states effectively, students must at minimum possess self-awareness or self-evaluation about which states would be more/less advantageous for a particular task and how to produce them.
Thus, to promote students' reading comprehension and achievements, researchers have strongly underscored the importance of explicitly training students to both monitor and regulate their own motivational processes (Reynolds, 2017; Schwinger and Otterpohl, 2017). However, prior research has not yet sufficiently investigated when best to embed explicit metamotivational scaffolding to enhance learners' active, effective engagement with science reading comprehension tasks and to promote their science literacy.
Timing of Metamotivational Self-Questioning Support
Schon (1996) distinguishes between in-action and on-action reflective self-questioning. Reflection-in-action describes interaction with a “live” problem as it unfolds during task performance—also termed by Raelin (2001) as “contemporaneous reflection,” occurring at the moment. Reflection-on-action describes activation of reflection processes after task performance, which enables learners to construct and evaluate explicit theories of action for solving future scientific problems—termed by Raelin (2001) as “retrospective reflection” for looking back at the experience. According to Seibert (1999), students tend to deal with live problems spontaneously, using their tacit knowledge, even when problems elicit uncertainty or surprise; hence, at the in-action (during-task) phase, reflection processes are generally not activated. In contrast, on-action (after-task) reflection processes are activated whenever a problem contains ambiguity or conflict because learners must consciously confront their tacit theories of action to evaluate their problem solution.
While Schon's work is highly regarded, it does not refer to the activation of before-action reflection processes (Hackett, 2001). Reflection-before-action has been described as a “pre-reflection” stage (Dewey, 1997) or as “anticipatory reflection,” often occurring at the planning stage (Raelin, 2001). In pre-task reflection, teachers may provide students with self-directed questions that give external structure to the self-regulation process in the form of a general work routine.
Researchers have begun to construct innovative instructional methods for science students based on metamotivational reflection as supported by self-directed questioning (Puteh and Ibrahim, 2010; Salinger, 2010; Schwinger and Stiensmeier-Pelster, 2012; Bråten et al., 2013; Michalsky, 2013; Frankel, 2016). However, empirical studies to date have not yet simultaneously compared the three possible metamotivational scaffolding timeframes corresponding with the forethought, performance, and retrospection phases of self-regulated learning of Zimmerman (1990).
Research to date examining before-task scaffolding for self-regulated learning, delivered only at the reflection-before-action phase (per Dewey, 1997; Raelin, 2001), has not sufficiently investigated science reading or scaffolding that specifically focused on metamotivation. In the math and literature learning contexts, pre-task scaffolding given to high-school students to support their cognitive and metacognitive (but not motivational) self-regulated learning (i.e., before math problem solving or literature reading comprehension) has been found to promote learners' motivation to perform the task as well as their academic-domain achievements (Mevarech and Kramarski, 2017; Reynolds, 2017). These findings for before-task cognitive and metacognitive scaffolding suggest the possible benefits of pre-action self-questioning of a metamotivational nature. Scholer and Miele (2016) have recommended using metamotivation processes before the learning action to enhance learners' fuller engagement in tasks and higher self-efficacy for successfully activating cognitive processes, compared with conditions where metamotivation is used during and or after cognitive processes.
Little research has examined the efficacy of retrospective metamotivational self-questioning in the form of instructional scaffolding presented to learners only after completing their task (reflection-on-action per Schon, 1996). In one study, Scholer and Miele (2016) have shown that scaffolding for metamotivation reflection delivered after completing a science learning task improves university students' achievements, compared with students who do not receive such scaffolding.
With regard to during-task scaffolding, delivered only at the reflection-in-action phase (per Schon, 1996), comparative research has shown that high school students exposed to metamotivational self-questioning instructional methods during their reading of microbiology texts significantly outperform their peers who do not receive metamotivational self-questioning support, as assessed on measures of general science literacy and domain-specific microbiology knowledge (du Boulay, 2011; Mahdavi and Tensfeldt, 2013; Michalsky, 2013).
In one of the rare studies to date comparing the three timeframes for self-regulatory scaffolding of reading comprehension, Michalsky (2013) has presented IMPROVE self-questions to elementary school students before, during, and after reading science texts (albeit addressing metacognitive regulation, not motivational regulation). Findings have indicated that these younger children perform best when they receive the metacognitive self-questioning after their text reading, as assessed by measures of general and domain-specific science achievements and metacognitive awareness. Prior research has also shown improvements in all three components of self-regulation [cognition, metacognition, and motivation as assessed using the measure of Pintrich (1999)] when secondary school students adapt their trained reading methods to a specific text via the use of a preplanned external procedure that helps structure students' processes of cognitive and metacognitive (but not motivational) self-regulation as implemented before, during, and after the reading task (Souvignier and Mokhlesgerami, 2006).
Current Study Objectives
In line with the call of Lajoie (2005) to examine not only what and how to scaffold for reading comprehension but also when to scaffold and when to fade scaffolding, the current study's preliminary exploration aims to narrow the gaps mentioned earlier in the literature. The present quasi-experiment compared the effectiveness of the three timeframes for introducing metamotivational self-questioning support—before, during, or immediately after reading scientific texts—as compared with a control group receiving no metamotivational support at all. To comprehensively assess the impact of the four different intervention conditions, gains were measured in general science literacy, domain-specific knowledge, and metamotivational skills following the intervention.
This study focused on 10th graders because, during adolescence, students' engagement in and motivation for reading decrease (Wigfield et al., 2016). Specifically, with regard to the field of science literacy, students often report a strong dislike of reading, which may be attributed to age-related decreases in self-efficacy beliefs about their adequacy of skills and knowledge for comprehending increasingly complex secondary school science texts (Guthrie and Coddington, 2009; Cunningham and Zibulsky, 2014).
Based on prior literature indicating that supporting students' motivation for reading enhances scientific text comprehension (e.g., Scholer and Miele, 2016; Reynolds, 2017), all three metamotivational groups were expected to outperform the control group on all three dependent variables. Due to the paucity mentioned earlier of comparative research regarding the timing of metamotivational scaffolding for secondary students' scientific text comprehension (see, for example, rare studies on motivation and cognition aspects by Souvignier and Mokhlesgerami, 2006; Logan et al., 2011), no explicit assumption was formulated about the effects of the “before” vs. “during” vs. “after” metamotivational scaffolding approaches embedded at three different phases of the scientific text reading.
Method
Participants
Participants were 202 10th graders (102 boys, 100 girls; mean age: 15.5 years, SD = 0.63) who attended eight heterogeneous classes belonging to different districts. The classes were randomly selected from 21 Israeli high schools whose science teachers participated in a long-term 4-months in-service training program concerning the 10th-grade Invitation to Scientific Inquiry science curriculum. The following parameters were similar across all high schools: size (two to three classes per grade level for grades 7–12), middle-class socioeconomic status as defined by the Israel Ministry of Education (Central Bureau of Statistics, 2006), and students' pretest science achievement levels.
The eight teachers who underwent the current training and delivered the intervention to their 10th-grade science classes (five females, three males; mean age: 35 years, SD = 0.82) held a science teaching certification, an academic degree in science, and more than 8 years of experience in science teaching. Two teachers each were randomly assigned to the four intervention conditions: those receiving metamotivational self-questioning support before text reading (“BEF,” n = 52), during text reading (“DUR,” n = 50), or after text reading (“AFT,” n = 54), and those who did not receive any self-questioning or metamotivational support (“control,” n = 46). Statistical analyses conducted at pretest on demographic variables for teachers (sex, age, and years of teaching experience) and students (sex and age) and on all study variables for students yielded no statistically significant intergroup differences.
Science Study Unit
All students in all eight classrooms studied the “The World of Microorganisms” study unit for 3.5 months during 10th grade as part of a series of science study units entitled Invitation to Scientific Inquiry (National Research Council, 2015). All eight classrooms used the same textbook and read the exact same scientific texts comprising the inquiry-based “Microbiology” unit for three lessons per week over 14 weeks. The experiment was delivered in six of the eight classrooms, in only one of their three weekly microbiology lessons. Once weekly, the six teachers in the “BEF,” “DUR,” and “AFT” groups (two teachers per group) implemented their assigned metamotivational self-questioning scaffolding method for their 10th graders' reading comprehension of scientific texts, whereas the two teachers in the control condition used traditional instruction for all three weekly lessons.
For the first 2 weeks of the 14-weeks period, the two classrooms comprising the control group introduced standard instructional methods for scientific text comprehension, with no metamotivational scaffolding. In the remaining six of the eight classrooms, the teachers in the three experimental groups dedicated their first two weekly lessons to introducing their assigned IMPROVE self-questioning model (BEF, DUR, or AFT) that students would utilize before, during, or after reading scientific texts, respectively. During these two introductory training lessons, the instructors in the three experimental groups provided demonstrations and modeling of their respective metamotivational scaffolding conditions to initially train students in the utilization of these scaffolds for attempts to monitor and manage their motivation and efforts.
For all four learning conditions, each of the remaining 12 lessons contained three parts: outline, practice, and summary phases (see Appendix A). In the practice phase that comprised most of each lesson (~30 min), all students practiced comprehension of the lesson's scientific texts and, based on their reading, worked to solve related scientific exercises. Each student in all four groups received a personal copy of the printed worksheet presenting that lesson's microbiology text and accompanying exercises. The scientific texts were related to the microbiology phenomena that all students were learning in their other two weekly classes. For the three treatment conditions only, the worksheets additionally presented the metamotivational scaffolds, differing between the BEF, DUR, and AFT groups only in the timing of their embedment.
As shown in Table 1, for all four groups, each round of engagement with a science reading comprehension task comprised two phases: (a) individual reading and individual exercise performance, followed by (b) small-group discussion and joint reflection on the individual students' exercise solutions. Only in the BEF/DUR/AFT conditions (but not in the control condition) did students receive and respond to metamotivational training scaffolds during these two engagement phases.
Table 1. Four groups' training conditions (Modeled in lessons 1 and 2 and practiced in lessons 3 and 14).
Three Experimental Metamotivational Treatment Conditions
Students in the three experimental conditions (BEF, DUR, and AFT) were all exposed to the same series of four self-addressed metamotivation questions, based on the IMPROVE method of Kramarski and Mevarech (1997) as updated by Michalsky (2013). These four self-questioning scaffolds for each microbiology reading comprehension task pertained to Comprehension (task knowledge), Connection (inter-task knowledge), Strategies (strategy knowledge), and Reflection (self-knowledge). The three experimental training groups' metamotivational scaffolds differed from one another only in (a) the timing of their embedment in the worksheets (e.g., see Appendix B for the DUR group) and (b) their use of appropriate tense: future tense for the BEF group, present tense for the DUR group, and past and future tense for the AFT group (see left column of Table 2).
Table 2. IMPROVE metamotivational self-questions, with sample excerpts from individual phase of student training lessons, by treatment group.
During the two introductory training lessons (Lessons 1 and 2), the instructors in the three experimental groups demonstrated to students how to utilize the four IMPROVE self-questioning scaffolds throughout their individual and small-group phases of engagement with the study unit's assigned science texts and to accompany reading comprehension exercises, according to each condition's timing for embedment (see Table 1).
During reading task practice for the experimental conditions in Lessons 3–14, the individual engagement phase was accompanied by an individual responding to the four metamotivational self-questioning scaffolds (see Table 2 for excerpts from students' utilization of these training scaffolds in the individual phase). Then, the small-group engagement phase in the three groups was accompanied by joint discussion and reflection on their metamotivational responses.
To be noted, the Comprehension, Connection, and Reflection self-questions scaffolded students' motivational monitoring or awareness, whereas the Strategy question uniquely scaffolded students' attempts to control and manage their motivation and efforts. Thus, to scaffold the training procedure for the Strategy question in each lesson, each student in all three treatment conditions also received a personal copy of a user-friendly printed card cueing them about the repertoire of eight possible strategies for managing their own motivation (e.g., how to apply self-talk in their science reading task; see left column of Table 3). The instructor had modeled and exemplified these eight motivational management strategies in Lessons 1 and 2, based on Schwinger and Otterpohl (2017), according to each condition's timing for the Strategy self-question. Each treatment condition's worksheets and cards were included in the teachers' guidebook.
Table 3. Repertoire of eight motivational management strategies.
Teacher Training
To prevent treatment diffusion and compensatory rivalry, each pair of teachers (in the BEF, DUR, AFT, and control conditions) participated in a separate 2-days (6-h) in-service training program on the instruction of scientific text comprehension. The training instructor (the author) holds expertise in science text reading comprehension and the different metamotivational support conditions.
The first day of training was the same for all four conditions (BEF, DUR, AFT, and control), emphasizing the importance of strengthening students' science literacy and discussing the possible difficulties students encounter in comprehending scientific texts. The second day differed according to the assigned condition. The two teachers in the control group received the standard national 10th-grade approaches for reading science texts relevant to the study unit, and the instructor demonstrated teaching methods for enhancing scientific text comprehension in the classroom. Each of the three metamotivational conditions (two teachers each) received an introduction to the rationale and techniques of their assigned IMPROVE scaffolding method (BEF, DUR, or AFT). For these six teachers, the instructor accentuated the importance of metamotivation for encouraging scientific text reading, demonstrated the assigned timing and procedure for the IMPROVE self-questions' implementation in the classroom, and also modeled the use of the eight motivational management strategies based on Schwinger and Otterpohl (2017).
Throughout both days of training for all eight teachers, the instructor strongly emphasized the benefit of encouraging students to initiate discourse with their small-group team members through instructions such as: “Discuss your scientific ideas and reasoning with your team” or “Explain your answers to your peers.”
Fidelity
To ensure teachers' adherence to the scaffolding methods, all eight classrooms were observed by the author every other week across the 12-weeks experiment (8 classes × 6 observations = 48 observations in total). For the three treatment conditions (BEF, DUR, and AFT), observations were conducted in the one weekly biology lesson (out of three) when the teachers applied their assigned metamotivational scaffolding. For the control group, the observed lessons were selected randomly. After every observation, the instructor gave feedback to each observed teacher, answered teachers' questions, and offered recommendations for improvement if necessary. Overall, the teachers adhered well to the training they had received, both (a) regarding the microbiology unit's correlation to the standard national science curriculum and pedagogic inquiry strategies and (b) regarding their assigned scaffolding approach (or none) for reading scientific texts.
Assessment Measures
Three measurements were each completed by students at the pretest and posttest intervals.
Domain-Specific Microbiology Test
This 22-item domain-specific test was designed by the National Science Committee of the Israel Ministry of Education (2015) to examine students' knowledge of “The World of Microorganisms” science curriculum. The test included 10 multiple-choice items such as “Which of these statements is not true about HIV?” giving the following four choices [the correct response is “c”]: (a) It has a long incubation time, causing it to be able to remain in the host for a long time before discovery; (b) It is a retrovirus; (c) It infects all human cells; (d) There is a high rate of mutation in the HIV virus. Each of the 10 multiple-choice items was scored as either 0 (incorrect) or 4 (correct), with the total score for these items ranging from 0 to 40.
The scoring for each of the test's 12 open-ended questions (e.g., “Write two viruses' characteristics”) ranged from 0 (incorrect) to 5 (full answer), with the total score for all open-ended questions ranging from 0 to 60. Two trained judges with expertise in science knowledge coded students' responses. Inter-judge reliability, calculated for the same 35% of the responses coded by both judges, yielded reliability coefficients ranging from r = 0.88 to 0.97 for all levels. Total microbiology scores were 0–100. The correlation between the pretest and posttest scores was r = 0.81.
General Test of Science Literacy
This 15-item test was designed for the purpose of the current study based on PISA 2015 science literacy tests (Organisation for Economic Co-operation Development, 2017), tapping into students' “literacy” in the five major components of scientific experiments (see Table 4). Two trained judges with expertise in science knowledge coded students' responses. Inter-judge reliability, calculated for the same 40% of the responses coded by both judges, yielded reliability coefficients ranging from r = 0.81 to 0.93 for all levels. Total general literacy scores were 0–100. The correlation between the pretest and posttest scores was r = 0.85.
Table 4. Sample items for five components on 15-item general test of science literacy.
Motivational Regulation Strategies in Reading Science Texts
Students' self-reported use of metamotivational management efforts was assessed using the 30-item Motivational Regulation Strategies Questionnaire of Schwinger et al. (2009), adapted to the specific context of reading scientific literature. Sample items and reliabilities for the eight different motivational regulation strategies are presented in Table 3.
Procedure
The research reported in this study involving human participants was approved by the Research Ethics Board at Bar-Ilan University in accordance with ethical standards comparable to the 1964 Helsinki declaration. The eight participating teachers in the current study were randomly selected from 21 teachers who volunteered for further training and research on scientific text comprehension, following their participation in the Invitation to Scientific Inquiry in-service training program held in central Israel. The eight teachers were then randomly assigned to one of the four intervention conditions (two teachers each to the BEF, DUR, AFT, and control conditions). The purpose of the study and the existence of the other intervention conditions were masked; teachers were only informed by the training instructor that they were participating in an experiment on new pedagogical approaches to enhance scientific text comprehension.
All students were administered the three pretests (on science literacy, domain-specific microbiology knowledge, and motivational regulation strategies for reading science texts) during their biology lessons within the first 3 weeks of the school year, immediately before beginning the 3-months “The Microorganisms' World” science learning unit. At the end of the 3-months unit, all students completed the three measures again in their biology classrooms (posttests).
Data Analyses
One-way within-subject analyses of variance (ANOVA) with repeated measures were conducted, with treatment (four groups) as the independent variable and with posttest performance measures (for the three tests separately) as the dependent variables. Analyses of the total scores were followed by analyses of component subscales. Post hoc comparisons were conducted as needed in the form of pairwise contrasts. In addition, correlations were calculated among the three dependent variables at the end of the study (Time 2) for each of the four research groups.
Results
Domain-Specific Science Knowledge Test on Microbiology
Table 5 presents the means, standard deviations, and adjusted means for students' total scores on the Test of Science Knowledge by time and treatment. As seen on the table, at pretest, no significant differences emerged between the treatment groups with regard to microbiology knowledge, F(1,201) = 13.56, η2 = 0.19, p > 0.18. This validated the four groups' equivalent baseline scores for microbiology knowledge. The one-way repeated measures ANOVA revealed a significant main effect for Time, MSe = 5.3, F(1,201) = 11.3, η2 = 0.36, p < 0.001, and a significant Time × Treatment interaction, F(1,201) = 31.2, η2 =0.15, p < 0.001. However, at the posttest interval, significant intergroup differences did emerge. Post hoc analyses of the adjusted mean scores based on pairwise comparison t-tests indicated that the BEF group (M = 78.31) significantly outperformed all other groups; the AFT group (M = 70.32) significantly outperformed the DUR group (M = 74.11); and the control group (M = 65.17) attained the significantly lowest microbiology knowledge scores (all p < 0.05).
Table 5. Students' means, standard deviations, and adjusted mean scores on the test of domain-specific science knowledge, by time (pre/post) and treatment.
General Science Literacy
Table 6 presents the means, standard deviations, and adjusted means for students' total scores and subscale scores on the Test of Science Literacy by time and treatment. At pretest, no significant differences emerged between the treatment groups regarding the total score or any of the five components of general science literacy, F(1,201) = 18.32, η2 = 0.13, p > 0.22. This validated the four groups' equivalent baseline scores for general science literacy. As presented on the table, the one-way repeated measures ANOVAs revealed a significant main effect for Time and a significant Time × Treatment interaction for the total score, MSe = 25.16, p < 0.001, and for all five of the literacy components, MSe = 28.36, p < 0.001. As illustrated in Figure 1, post hoc analyses of the adjusted mean scores based on the pairwise comparison t-test indicated that on the total score, the BEF group (M = 74.98) significantly outperformed all other groups; the AFT research group (M = 62.96) significantly outperformed the DUR group (M = 67.84); and the control group (M = 58.98) attained the significantly lowest mean literacy scores (all p < 0.05). As seen in the figure, the same pattern of findings emerged for all five science literacy components.
Table 6. Means, standard deviations, and cohen's d effect sizesa on general test of science literacy, by time and treatment, with significant effects.
Figure 1. Mean scores on science literacy total and components, by time and treatment. BEF = metamotivation intervention before text reading; DUR = metamotivation intervention during text reading; AFT = metamotivation intervention after text reading; Control = no metamotivation intervention.
Motivational Regulation Strategies for Science Text Reading
Table 7 presents the means, standard deviations, and adjusted means for students' total scores and eight strategy scores on the Motivational Regulation Strategies Test, by time and treatment. At pretest, no significant differences emerged between the treatment groups regarding the total score or any of the eight strategies, F(1,201) = 18.63, η2 = 0.23, p > 0.24. This validated the four groups' equivalent baseline scores on motivational regulation strategies. As presented in the table, the one-way repeated measures ANOVAs revealed a significant main effect for Time and a significant Time × Treatment interaction for the total score, MSe = 36.12, p < 0.001, and for all eight motivational management strategies, MSe = 23.25, p < 0.001. Post hoc analyses of the adjusted mean scores based on the pairwise comparison t-test indicated that on the total score, the BEF group (M = 4.12) significantly outperformed all other groups; the AFT group (M = 3.24) significantly outperformed the DUR group (M = 3.77); and the control group (M = 2.91) attained the significantly lowest mean scores in motivational regulation (all p < 0.001). As seen in Table 7, the same pattern of findings emerged for all eight strategies.
Table 7. Means, standard deviations, and cohen's d effect sizesa of motivational regulation strategies, by time and treatment, with significant effects.
Correlations Among Dependent Variables at Time 2
Table 8 presents the results of the correlation analysis conducted among science literacy, domain-specific microbiology knowledge, and metamotivation regulation strategies for each of the four research groups at the end of the study. Significantly higher correlations (using Fisher's transformation of r to Z) were found in the BEF group than in the other three groups. The AFT group revealed significantly higher correlations than the DUR group. The control group showed the lowest correlations among dependent variables, which were all non-significant.
Table 8. Correlations (Fisher's transformation of r to Z) among dependent variables in the four research groups at time 2.
Discussion
Findings from the current quasi-experiment clearly highlighted the advantage of metamotivational scaffolding's embedment in 10th-graders' scientific text reading over the effectiveness of standard instructional methods that do not include any such scaffolding. Namely, as expected, all three student groups who received support for motivational self-regulation were found to outperform the control group on all studied variables: not only on their ability to regulate their own motivation to read about science but also on their general and domain-specific science achievements. With regard to the main focus of this study—identifying the optimal phase for embedding metamotivational scaffolding—high school students who received such scaffolding before the reading task significantly outperformed the other two groups who received metamotivational scaffolding either during or after reading, regarding all of the outcomes assessed in the present study. More extensive consideration is given next to these findings.
Benefit of Metamotivational Scaffolding Over Standard Instruction
The advantage found for metamotivational scaffolding (in the BEF, DUR, and AFT groups) over standard instructional methods (in the control group) coincides with prior studies showing that explicit scaffolding is a necessity when training students to self-regulate their motivation while reading scientific texts (Souvignier and Mokhlesgerami, 2006; Michalsky, 2013; Hsu et al., 2016; McNamara, 2017). This outcome also substantiates the claim that mere exposure to scientific texts is insufficient on its own (Ozuru et al., 2009). As Hartman (2001, p. 56) has argued [emphasis appeared in the original]:
Teachers should not be satisfied with putting students in situations which require them to use any strategy they want students to use. Practice isn't enough. It is also important to provide explicit instruction in when, why and how to use the strategy; students need to understand the rationale and effective procedures for the strategy so that they can recognize appropriate contexts for its use, so that they have criteria for evaluating their strategy, and so they can self-regulate its use.
The three treatment groups' higher gains in motivational self-regulation than the control group may be attributed to the reflective processes inherent in answering the self-addressed metamotivation questions. Namely, contemplating the Comprehension, Connection, and Reflection self-questions may have promoted students' self-awareness of their own motivation, whereas contemplating the Strategy self-question and cued repertoire of strategies may have promoted their self-management of that motivation. These metamotivational monitoring and control processes (Veenman et al., 2006) may, in turn, facilitate students' science achievements.
Benefits of Metamotivational Scaffolding Given Before Science Text Engagement
The BEF group of 10th graders exposed to metamotivational scaffolding before they began reading each text and its accompanying comprehension exercises significantly outperformed the other two metamotivation groups (DUR and AFT). This advantage for the BEF group occurred, although all three treatment groups had received the same training and scaffolds (for self-regulatory motivational reflection via the four IMPROVE self-questions and for metamotivational management via the eight-strategy repertoire) at some point in their engagement with the same science texts and exercises.
This current finding on high school students differs from similar prior research outcomes focused on the metacognitive rather than motivational component of self-regulation among younger students (Michalsky, 2013). Further research is needed to determine if the different outcomes (highest effectiveness of pre-reading scaffolding for secondary students in the current study vs. highest effectiveness of post-reading scaffolding for elementary students in Michalsky, 2013) may possibly be attributable to factors related to students' age and/or to the metacognitive vs. metamotivation type of self-questioning scaffolding. For example, perhaps the adolescents' age-related cognitive abstraction capability or short-term working memory (Souza and Oberauer, 2016) may have enabled the high schoolers to maintain the IMPROVE self-questions in mind while engaging in their reading tasks, whereas the cognitive load may have been too heavy for those younger children who received before-task scaffolding in Michalsky (2013).
It may be speculated that answering the motivation-oriented self-questions before approaching the reading task may have served to focus the current 10th-grade BEF group's attention onto their motivational state across the entire ensuing reading context. Such initial mapping of their metamotivational monitoring may thereby have helped them to identify later when they were, or were not, experiencing an optimal motivational state for learning (Brown et al., 2016). Such better self-awareness, beginning in the starting phase of the learning task, in turn, may have fostered their ability to search for effective strategies and actions among their learned repertoire of metamotivational strategies (i.e., metamotivational control), to induce that optimal state in themselves all along with the upcoming reading task and comprehension exercises (Pintrich, 2002; Wolters, 2003; Miele and Scholer, 2018).
In addition, according to the chronological model of self-regulated learning phases of Zimmerman (2000), the “starting” forethought phase involves planning strategies such as task analysis or goal setting and is mainly influenced by learners' self-efficacy (belief in their competence, Bandura, 1977) regarding the learning task. Students with high self-efficacy have been shown to work diligently to master difficult scientific reading tasks, using their cognitive strategies productively (Zimmerman and Schunk, 2013). Perhaps, the early timing of the metamotivational self-questions in the BEF group enhances 10th-graders' optimism and confidence in their ability to cope with potential difficulties that may arise during engagement with the upcoming challenging science task. It may be that building up high self-efficacy leads, in turn, to better control over their own ensuing motivations (Butler et al., 2017). Considering self-efficacy beliefs' documented links to strategy use, self-regulation, and intrinsic motivation in the reading context (Pintrich and De Groot, 1990; Zimmerman and Schunk, 2003), researchers would do well to include self-efficacy measures in future metamotivational methodologies.
Not only the BEF group but also the AFT group outperformed the DUR group on all three dependent variables—motivational regulation strategies, general science literacy, and domain-specific science achievements. This resembled a prior finding for metacognitive scaffolding given during science text reading to elementary school students (Michalsky, 2013). Perhaps the lowest outcomes for the scaffolding provided during reading comprehension processes were attributable to learners' heavy cognitive load in this instruction condition. Bunch and Earl Lloyd (2006) have argued that cognitive load theory and cognitive load management are fundamental in reading comprehension because science texts provide large and complex amounts of information. Cognitive load theory posited that effective scaffolding facilitates learning by “directing cognitive resources toward activities that are relevant to learning rather than toward preliminaries to learning” (Chandler and Sweller, 1991, p. 294). Chandler and Sweller have noted that unnecessarily forcing learners to work with disparate sources of mutually referring information leads to ineffective scaffolding and to an increase in their cognitive load during reading. Therefore, scaffolding learners to utilize specific instructional materials before commencing their learning, as given in the current BEF group, may allow learners the freedom to employ any of the scaffolds at any time as they deem necessary to promote their understanding and solving of the problem.
The groups' patterns of correlations after the experiment—among their motivational regulation strategies, general science literacy, and domain-specific science achievements using the Fisher transformation—appear to corroborate the advantage found for the BEF reading group (showing the highest correlations) over the other three groups, followed by the AFT, DUR, and control groups, respectively. Although these correlational data do not permit assumptions about causality, stronger relationships may attest to more effective reciprocal influences between students' motivational self-regulation for science reading and their science literacy and educational achievement outcomes. Greater motivational regulation may lead to greater effort and persistence, which result in better instructional performance and vice versa (Bandura, 1977; Guthrie and Coddington, 2009). In this sense, the scaffolding of students' motivation at the before phase of reading science texts appears to have the greatest value for leveraging the important links between metamotivation, reading comprehension, and science achievements.
Study Limitations, Implications
As a preliminary exploration of the timing of metamotivational scaffolding, the current study requires future validation. This study's utilization of only one self-questioning method (IMPROVE) suggests that future researchers would do well to expand investigation on timing to various additional kinds of metamotivational scaffolding methods such as prompts, teacher tutoring, and so on. Likewise, the stronger benefit of metamotivational scaffolding at the before phase of microbiology text reading should be examined regarding the diverse scientific content matter and non-science domains. Future researchers may also wish to scrutinize the role played by text difficulty, domain familiarity, and prior knowledge on how students utilize metamotivational scaffolding provided at different learning phases. To further explore scaffolding methods, a fine-grain inquiry may also help identify the relative effectiveness of the different metamotivational management strategies for enhancing science students' outcomes.
Furthermore, considering that the current outcomes contradict those found for elementary school children, the same methodology should be used simultaneously with students across age groups to elucidate developmental trajectories while also examining sex differences. The present study did not show any sex differences on any of the study variables, but prior research has indicated that girls tend to outperform boys on reading comprehension, whereas boys have a distinctive advantage over girls with regard to scientific interest and literacy (e.g., Organisation for Economic Co-operation Development, 2015b). Finally, qualitative methods such as think-aloud processes rather than quantitative self-reports may help clarify metamotivation experiences, skills, and strategies at different phases of text reading.
Conclusions
Although no explicit assumptions could be formulated about the comparative effectiveness of the three timeframes due to the paucity of research in this area, the current preliminary study's outcomes highlight the potential impact of the current metamotivation instructional framework. Especially when delivered at the reflection-before-action stage for looking ahead and also at the reflection-on-action stage for looking back, metamotivational scaffolding may offer important means to promote science students' capacities and to meet the growing challenges in science teaching schoolwork.
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
Ethics Statement
The studies involving human participants were reviewed and approved by Bar Ilan University Ramat-Gan 5290002. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Conflict of Interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.658027/full#supplementary-material
References
Ahmadi, M. R., Ismail, H. N., and Abdullah, M. K. K. (2013). The importance of metacognitive reading strategy awareness in reading comprehension. English Lang. Teach. 6, 235–244. doi: 10.5539/elt.v6n10p235
Ali, A. M., and Razali, A. B. (2019). A review of studies on cognitive and metacognitive reading strategies in teaching reading comprehension for ESL/EFL learners. English Lang. Teach. 12, 94–111. doi: 10.5539/elt.v12n6p94
Azhari, A. D. (2020). The correlation between students' motivation and students' reading ability. J. Res. Educ. Learn. 15, 971–981. Available online at: http://riset.unisma.ac.id/index.php/jp3/article/view/5944
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84:191. doi: 10.1037/0033-295X.84.2.191
Boekaerts, M. (1995). Self-regulated learning: Bridging the gap between metacognitive and metamotivation theories. Educa. Psych. 30, 195–200. doi: 10.1207/s15326985ep3004_4
Bråten, I., Ferguson, L. E., Anmarkrud, Ø., and Strømsø, H. I. (2013). Prediction of learning and comprehension when adolescents read multiple texts: the roles of word-level processing, strategic approach, and reading motivation. Read. Writ. 26, 321–348. doi: 10.1007/s11145-012-9371-x
Brown, D. M., Teseo, A. J., and Bray, S. R. (2016). Effects of autonomous motivational priming on motivation and affective responses towards high-intensity interval training. J. Sports Sci. 34, 1491–1499. doi: 10.1080/02640414.2015.1119301
Bunch, R., and Earl Lloyd, R. (2006). The cognitive load of geographic information. Prof. Geograph. 58, 209–220. doi: 10.1111/j.1467-9272.2006.00527.x
Butler, D. L., Schnellert, L., and Perry, N. E. (2017). Developing Self-Regulating Learners. Toronto, ON: Pearson Canada.
Central Bureau of Statistics. (2006). Characterization and Classification of Local Authorities by the Socio-Economic Level of the Population. Available online at: http://www.cbs.gov.il/
Chandler, P., and Sweller, J. (1991). Cognitive load theory and the format of instruction. Cogn. Instru. 8, 293–332. doi: 10.1207/s1532690xci0804_2
Corno, L. (1993). The best-laid plans: modern conceptions of volition and educational research. Educat. Res. 22:14. doi: 10.3102/0013189X022002014
Cunningham, A. E., and Zibulsky, J. (2014). Book Smart: How to Support Successful, Motivated Readers. Oxford: Oxford University Press.
De Smedt, F., Rogiers, A., Heirweg, S., Merchie, E., and Van Keer, H. (2020). Assessing and mapping reading and writing motivation in third to eight graders: a self-determination theory perspective. Front. Psychol. 11:1678. doi: 10.3389/fpsyg.2020.01678
Deliany, Z., and Cahyono, B. Y. (2020). Metacognitive reading strategies awareness and metacognitive reading strategies use of EFL university students across gender. Studies English Lang. Educ. 7, 421–437. doi: 10.24815/siele.v7i2.17026
du Boulay, B. (2011). Motivationally intelligent educational systems: The contribution of the human centred technology research group. Technol. Instruc.Cogn. Learn. 8, 229–254.
Dutke, S., Grefe, A. C., and Leopold, C. (2016). Learning from scientific texts: Personalizing the text increases transfer performance and task involvement. Europ. J. Psych. Educ. 31, 499–513. doi: 10.1007/s10212-015-0281-6
Egloff, F. (2019). Students' reading ability moderates the effects of teachers' beliefs on students' reading progress. Front. Learn. Res. 7, 1–22. doi: 10.14786/flr.v7i1.336
Egloff, F., and Souvignier, E. (2020). Effects of teacher belief adaptivity on students' reading skills. Europ. J. Psychol. Educ. 35, 955–973. doi: 10.1007/s10212-019-00448-2
Farhana, E., Rutherford, T., and Lynch, C. F. (2020). “Investigating relations between self-regulated reading behaviors and science question difficulty,” in Proceedings of The 13th International Conference on Educational Data Mining, eds A. N. Rafferty, J. Whitehill, V. Cavalli-Sforza, and C. Romero. Available online at: https://educationaldatamining.org/files/conferences/EDM2020/papers/paper_160.pdf
Frankel, K. K. (2016). The intersection of reading and identity in high school literacy intervention classes. Res. Teach. English. 51, 37–59.
Graesser, A. C., León, J. A., and Otero, J. C. (2002). “Introduction to the psychology of science text comprehension,” in The Psychology of Science Text Comprehension, eds J. Otero, J. A. León, and A. C. Graesser (Mahwah, NJ: Erlbaum), 1–15.
Guthrie, J. T., and Coddington, C. S. (2009). “Reading motivation,” in Handbook of Motivation at School, eds K. Wentzel, and A. Wigfield (New York, NY: Routledge), 503–525. doi: 10.1037/t23360-000
Hackett, S. (2001). Educating for competency and reflective practice: fostering a conjoint approach in education and training. J. Workplace Learn. 13, 103–112. doi: 10.1108/13665620110388406
Hartman, H. J. (2001). “Teaching metacognitively,” in Metacognition in Learning and Instruction: Theory, Research and Practice, ed H. J. Hartman (Boston: Kluwer), 33–68. doi: 10.1007/978-94-017-2243-8_8
Hsu, Y. S., Yen, M. H., Chang, W. H., Wang, C. Y., and Chen, S. (2016). Content analysis of 1998–2012 empirical studies in science reading using a self-regulated learning lens. Int. J. Sci. Math. Educ. 14, 1–27. doi: 10.1007/s10763-014-9574-5
Israel Ministry of Education. (2015). Curriculum: Science and Technology Studies in High School. Jerusalem: Ministry of Education press.
Jian, Y. C. (2018). Reading instructions influence cognitive processes of illustrated text reading not subject perception: an eye-tracking study. Front. Psychol. 9:2263. doi: 10.3389/fpsyg.2018.02263
Kamil, M. L., Borman, G. D., Dole, J., Kral, C. C., Salinger, T., and Torgesen, J. (2008). Improving Adolescent Literacy: Effective Classroom and Intervention practices (IES Practice Guide: NCEE 2008-4027). Washington, DC: National Center for Education Evaluation and Regional Assistance.
Kelley, M. J., and Decker, E. O. (2009). The current state of motivation to read among middle school students. Read. Psychol. 30, 466–485. doi: 10.1080/02702710902733535
Krajcik, J. S., and Sutherland, L. M. (2010). Supporting students in developing literacy in science. Science 328, 456–459. doi: 10.1126/science.1182593
Kramarski, B., and Mevarech, Z. R. (1997). Cognitive-metacognitive training within a problem-solving based Logo environment. Br. J. Educ. Psych. 67, 425–445. doi: 10.1111/j.2044-8279.1997.tb01256.x
Kuhl, J. (2000). “A functional-design approach to motivation and self-regulation: The dynamics of personality systems interactions,” in Handbook of Self-Regulation, eds M. Boekaerts, P. R. Pintrich, and M. Zeidner (San Diego, CA: Academic Press), 111–169. doi: 10.1016/B978-012109890-2/50034-2
Lai, M. K., Wilson, A., McNaughton, S., and Hsiao, S. (2014). Improving achievement in secondary schools: impact of a literacy project on reading comprehension and secondary school qualifications. Read. Res. Q. 49, 305–334. doi: 10.1002/rrq.73
Lajoie, S. P. (2005). Extending the scaffolding metaphor. Instruct. Sci. 33, 541–557. doi: 10.1007/s11251-005-1279-2
Li, L., Liu, Y., Peng, Z., Liao, M., Lu, L., Liao, H., et al. (2020). Peer relationships, motivation, self-efficacy, and science literacy in ethnic minority adolescents in China: a moderated mediation model. Child. Youth Serv. Rev. 119, 105–524. doi: 10.1016/j.childyouth.2020.105524
Liew, J., Valiente, C., Hernández, M. M., and Abrera, D. (2019). “Self-regulation and reactivity, school-based relationships, and school engagement and achievement,” in SAGE Handbook on Developmental Psychology and Early Childhood Education, eds D. Whitebread and M. McClelland (Cambridge), 42–62. doi: 10.4135/9781526470393.n3
Logan, S., Medford, E., and Hughes, N. (2011). The importance of intrinsic motivation for high and low ability readers reading comprehension performance. Learn. Indiv. Differ. 21, 124–128. doi: 10.1016/j.lindif.2010.09.011
Mahdavi, J. N., and Tensfeldt, L. (2013). Untangling reading comprehension strategy instruction: assisting struggling readers in the primary grades. Prev. School Fail. Altern. Educ. Child. Youth. 57, 77–92. doi: 10.1080/1045988X.2012.668576
McClelland, M. M., Cameron, C. E., Connor, C. M., Farris, C. L., Jewkes, A. M., and Morrison, F. J. (2007). Links between behavioral regulation and preschoolers' literacy, vocabulary, and math skills. Dev. Psych. 43, 947–959. doi: 10.1037/0012-1649.43.4.947
McNamara, D. S. (2017). Self-explanation and reading strategy training (SERT) improves low-knowledge students' science course performance. Discou. Processes. 54, 479–492. doi: 10.1080/0163853X.2015.1101328
Meniado, J. C. (2016). Metacognitive reading strategies, motivation, and reading comprehension performance of Saudi EFL students. English Lang. Teach. 9, 117–129. doi: 10.5539/elt.v9n3p117
Mevarech, Z., and Kramarski, B. (2017). Matemáticas críticas para las sociedades innovadoras: El papel de las pedagogías metacognitivas [Critical mathematics for innovative societies: The role of metacognitive pedagogies]. OECD. Available online at: https://www.oecd.org/publications/matematicas-criticas-para-las-sociedades-innovadoras-9789264273078-es.htm doi: 10.1787/9789264273078-es
Michalsky, T. (2013). Integrating skills and wills instruction in self-regulated science text reading for secondary students. Int. J. Sci. Educ. 35, 1846–1873. doi: 10.1080/09500693.2013.805890
Michalsky, T. (2020). Integrating video analysis of teacher and student behaviors to promote preservice teachers' teaching of meta-strategic knowledge. Metacogn. Learn. doi: 10.1007/s11409-020-09251-7
Michalsky, T., and Schechter, C. (2018). Teachers' self-regulated learning lesson design: integrating learning from problems and successes. Teacher Educ. 53, 101–123. doi: 10.1080/08878730.2017.1399187
Miele, D. B., and Scholer, A. A. (2018). The role of metamotivational monitoring in motivation regulation. Educat. Psychol. 53, 1–21. doi: 10.1080/00461520.2017.1371601
Morgan, P. L., and Fuchs, D. (2007). Is there a bidirectional relationship between children's reading skills and reading motivation? Except. Child. 73, 165–183. doi: 10.1177/001440290707300203
Murphy, P. K., Firetto, C. M., and Greene, J. A. (2017). Enriching students' scientific thinking through relational reasoning: seeking evidence in texts, tasks, and talk. Educ. Psychol. Rev. 29, 105–117. doi: 10.1007/s10648-016-9387-x
National Research Council. (2015). Enhancing the Effectiveness of Team Science. Washington, DC: National Academies Press.
Nguyen, T., Carnevale, J. J., Scholer, A. A., Miele, D. B., and Fujita, K. (2019). Metamotivational knowledge of the role of high-level and low-level construal in goal-relevant task performance. J. Person. Soc. Psych. 117:876. doi: 10.1037/pspa0000166
Oakhill, J. V., and Cain, K. (2012). The precursors of reading ability in young readers: evidence from a four-year longitudinal study. Scient. Studies Read. 16, 91–121. doi: 10.1080/10888438.2010.529219
Organisation for Economic Co-operation and Development. (2015a). PISA 2015 Results: Excellence and Equity in Education, Volume I. Available online at: https://www.oecd-ilibrary.org/docserver/9789264266490-en.pdf?expires=1614340152andid=id&accname=guest&checksum=B05C26DDD85FD8124549768E3D81C4BC
Organisation for Economic Co-operation and Development. (2015b). Universal Basic Skills: What Countries Stand to Gain. Available online at: http://doi.org/10.1787/9789264234833-en
Organisation for Economic Co-operation Development. (2017). PISA 2015: Assessment and analytical framework - science, reading, mathematic, financial literacy and collaborative problem solving (rev. ed.). Available online at: http://www.oecd-library.org/docserver/download/9817041e.pdf?expires=1521627125&id=id&accname=guest&checksum=2ED3BF3EE7E91AD7CB455C9A53C98262
Ozuru, Y., Dempsey, K., and McNamara, D. S. (2009). Prior knowledge, reading skill, and text cohesion in the comprehension of science texts. Learn. Instruct. 19, 228–242. doi: 10.1016/j.learninstruc.2008.04.003
Pamungkas, Z. S., Aminah, N. S., and Rosyid, F. N. (2018). Students critical thinking skill in solving scientific literacy using a metacognitive test based on scientific literacy. Jurnal Ilmiah Pendidikan Fisika Al-Biruni. 7:161. doi: 10.24042/jipfalbiruni.v7i2.2909
Pearson, P. D., Moje, E., and Greenleaf, C. (2010). Literacy and science: each in the service of the other. Science 328, 459–463. doi: 10.1126/science.1182595
Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. Int. J. Educ. Res. 31, 459–470. doi: 10.1016/S0883-0355(99)00015-4
Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory Into Practice. 41, 219–225. doi: 10.1207/s15430421tip4104_3
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educ. Psych. Rev. 16, 385–407. doi: 10.1007/s10648-004-0006-x
Pintrich, P. R., and De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. J. Educ. Psych. 82, 33. doi: 10.1037/0022-0663.82.1.33
Puteh, M., and Ibrahim, M. (2010). The usage of self-regulated learning strategies among form four students in the mathematical problem-solving context: a case study. Proced. Soc. Behav. Sci. 8, 446–452. doi: 10.1016/j.sbspro.2010.12.061
Raelin, J. A. (2001). Public reflection as the basis of learning. Manag. Learn. 32, 11–30. doi: 10.1177/1350507601321002
Rastegar, M., Kermani, E. M., and Khabir, M. (2017). The relationship between metacognitive reading strategies use and reading comprehension achievement of EFL learners. Open J. Modern Lingu. 7, 65–74. doi: 10.4236/ojml.2017.72006
Reynolds, D. (2017). Interactional scaffolding for reading comprehension: a systematic review. Literacy Res. Theory Method Practice. 66, 135–156. doi: 10.1177/2381336917718820
Roebers, C. M. (2017). Executive function and metacognition: Towards a unifying framework of cognitive self-regulation. Dev. Rev. 45, 31–51. doi: 10.1016/j.dr.2017.04.001
Salinger, T. (2010). Meeting the Literacy Needs of Students in Juvenile Justice Facilities: Strategy Guide. National Evaluation and Technical Assistance Center for Children and Youth Who Are Neglected, Delinquent, or At Risk. Available online at: https://files.eric.ed.gov/fulltext/ED577083.pdf
Sansone, C., and Thoman, D. B. (2005). Does what we feel affect what we learn? Some answers and new questions. Learn. Instru. 15, 507–515. doi: 10.1016/j.learninstruc.2005.07.015
Sansone, C., and Thoman, D. B. (2006). Maintaining activity engagement: individual differences in the process of self-regulating motivation. J. Personal. 74, 1697–1720. doi: 10.1111/j.1467-6494.2006.00425.x
Sason, H., Michalsky, T., and Mevarech, Z. R. (2020). Promoting science text comprehension via two self-questioning methods. Front. Psych. 11:595745. doi: 10.3389/fpsyg.2020.595745
Scholer, A. A., and Miele, D. B. (2016). The role of metamotivation in creating task-motivation fit. Motiv. Sci. 2:171. doi: 10.1037/mot0000043
Schon, D. A. (1996). Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in the Professions. San Francisco, CA: Jossey-Bass.
Schreiber, F. J. (2005). “Metacognition and self-regulation in literacy,” in Metacognition in Literacy Learning: Theory, Assessment, Instruction, and Professional Development, eds S. E. Israel, C. C. Block, K. L. Bauserman, & K. Kinnucan-Welsch (Mahwah, NJ: Lawrence Erlbaum), 215–239.
Schwinger, M., and Otterpohl, N. (2017). Which one works best? Considering the relative importance of motivational regulation strategies. Learn. Individual Differ. 53, 122–132. doi: 10.1016/j.lindif.2016.12.003
Schwinger, M., Steinmayr, R., and Spinath, B. (2009). How do motivational regulation strategies affect achievement: mediated by effort management and moderated by intelligence. Learn. Individ. Differ. 19, 621–627. doi: 10.1016/j.lindif.2009.08.006
Schwinger, M., and Stiensmeier-Pelster, J. (2012). Effects of motivational regulation on effort and achievement: a mediation model. Int. J. Educ. Res. 56, 35–47. doi: 10.1016/j.ijer.2012.07.005
Seibert, K. W. (1999). Reflection-in-action: tools for cultivating on-the-job learning conditions. Organiz. Dyn. 27, 54–65. doi: 10.1016/S0090-2616(99)90021-9
Skibbe, L. E., Connor, C. M., Morrison, F. J., and Jewkes, A. M. (2011). Schooling effects on preschoolers' self-regulation, early literacy, and language growth. Early Childhood Res. Q. 26, 42–49. doi: 10.1016/j.ecresq.2010.05.001
Souvignier, E., and Mokhlesgerami, J. (2006). Using self-regulation as a framework for implementing strategy instruction to foster reading comprehension. Learn. Instru. 16, 57–71. doi: 10.1016/j.learninstruc.2005.12.006
Souza, A. S., and Oberauer, K. (2016). In search of the focus of attention in working memory: 13 years of the retro-cue effect. Attention, Perception , & Psychophysics. 78, 1839–1860. doi: 10.3758/s13414-016-1108-5
van Rijk, Y., Volman, M., de Haan, D., and van Oers, B. (2017). Maximising meaning: creating a learning environment for reading comprehension of informative texts from a Vygotskian perspective. Learn. Environ. Res. 20, 77–98. doi: 10.1007/s10984-016-9218-5
Veenman, M. V., Van Hout-Wolters, B. H. A. M., and Afflerbach, P. (2006). Metacognition and learning: conceptual and methodological considerations. Metacog. Learn. 1, 3–14. doi: 10.1007/s11409-006-6893-0
Wigfield, A., Gladstone, J. R., and Turci, L. (2016). Beyond cognition: Reading motivation and reading comprehension. Child Dev. Persp. 10, 190–195. doi: 10.1111/cdep.12184
Wolters, C. A. (2003). Regulation of motivation: evaluating an underemphasized aspect of self-regulated learning. Educat. Psychol. 38, 189–205. doi: 10.1207/S15326985EP3804_1
Wolters, C. A. (2011). Regulation of motivation: contextual and social aspects. Teachers College Record. 113, 265–283.
Yore, L. D., and Tippett, C. D. (2014). “Reading and science learning,” in Encyclopaedia of Science Education, eds R. Gunstone (Dordrecht: Springer), 821–828. doi: 10.1007/978-94-007-6165-0_130-2
Zeidner, M., and Stoeger, H. (2019). Self-regulated learning (SRL): a guide for the perplexed. High Ability Studies 30, 9–51. doi: 10.1080/13598139.2019.1589369
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychology. 25, 3–17. doi: 10.1207/s15326985ep2501_2
Zimmerman, B. J. (2000). “Attaining self-regulation: a social cognitive perspective,” in Handbook of Self-Regulation, eds M. Boekaerts, P. R. Pintrich, & M. Zeidner (San Diego, CA: Academic Press), 13–39. doi: 10.1016/B978-012109890-2/50031-7
Zimmerman, B. J., and Schunk, D. H. (2003). “Albert Bandura: the scholar and his contributions to educational psychology,” in Educational Psychology: A Century of Contributions, eds B. J. Zimmerman and D. H. Schunk (Lawrence Erlbaum), 431–457.
Keywords: metamotivation scaffolding, motivational regulation strategies, science literacy, science knowledge, microbiology texts
Citation: Michalsky T (2021) When to Scaffold Motivational Self-Regulation Strategies for High School Students' Science Text Comprehension. Front. Psychol. 12:658027. doi: 10.3389/fpsyg.2021.658027
Received: 24 January 2021; Accepted: 23 March 2021;
Published: 14 May 2021.
Edited by:
Jin Su Jeong, University of Extremadura, SpainReviewed by:
Chung Kwan Lo, The Education University of Hong Kong, ChinaJack Holbrook, University of Tartu, Estonia
Copyright © 2021 Michalsky. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tova Michalsky, dG92YS5taWNoYWxza3kmI3gwMDA0MDtiaXUuYWMuaWw=