A Review of the Self-regulation Strategyinventory – Self-report (Srsi-sr)

Abstract

This work presents a systematic review of the Self-Regulation Strategy Inventory—Self Report (SRSI-SR). Theoretically grounded in Zimmerman's three-stage model of self-regulated learning (SRL), the SRSI-SR provides data almost learners' use of three broad types of self-regulation strategies. Google Scholar, PsycINFO, and ProQuest were searched to identify studies referencing the SRSI-SR; 18 studies met the inclusion criteria. Studies were coded based upon: 1) Likert calibration blazon employed, 2) type of study conducted, 3) student grade level, 4) school discipline area, and 5) other constructs also examined, such equally motivation and achievement. Overall, the SRSI-SR is emerging as a sound measure of SRL strategy use. Validity and reliability information, gaps in current knowledge about the measure, and future research recommendations are also discussed in more particular.

Share and Cite:

Tise, J. , Follmer, D. and Sperling, R. (2019) A Review of the Self-Regulation Strategy Inventory—Self-Report (SRSI-SR). Psychology, ten, 305-319. doi: ten.4236/psych.2019.103022.

ane. Introduction

Students who are self-regulated are metacognitively, motivationally, and behaviorally active participants in their own learning processes (Zimmerman, 1986). Learners who effectively cocky-regulate are able to optimize learning and performance outcomes (Cleary, Callan, & Zimmerman, 2012) because they plan and monitor their learning. These learners identify errors and change course through application of appropriate and constructive learning strategies. Students who effectively self-regulate possess provisional knowledge of these learning strategies, and deploy the advisable learning strategy for the given learning task. They also actively monitor and command their affect through all phases of learning.

Every bit such, self-regulated learning (SRL) requires coordinated and circuitous multidimensional processes. Constructive strategy employ represents particularly important component SRL processes. The constructive utilize of SRL strategies makes distinctive contributions to academic performance, above general ability (Zimmerman, 1990). Thus, the ability to identify and measure out students' strategy use is essential to inform instructional scaffolds and interventions that will promote successful learning.

Researchers measure SRL processes and strategies in multiple ways, including self-report surveys and interviews, think-aloud protocols, trace methodologies, error detection tasks, microanalytic protocols, diaries, and directly observations (Cleary, 2011; Schmitz, Klug, & Schmidt, 2011; Winne & Perry, 2000). Despite the existence of a diversity of SRL cess methods and approaches and despite recognized limitations of SRL inventories (east.g., Muis, Winne, & Jamieson-Noel, 2007; Winne, Hadwin, Stockley, & Nesbit, 1997; Winne & Perry, 2000), inventory measures such as the Cocky-Regulation Strategy Inventory―Cocky Report (SRSI-SR) remain a popular method among researchers, likely due to their accessibility, ease of administration, and ability to succinctly written report properties and findings.

Self-report inventories provide insight into learners' recollection and interpretations of their actions, every bit well as their accounts of cognitive and metacognitive processes (Turner, 1995; Winne & Perry, 2000). Students' responses can be targeted to focus on either SRL in situ or as representative of general learning traits, and there has been much discussion in the literature regarding whether or not SRL should be measured every bit a state or trait (e.thou., Winne & Perry, 2000). Typically, self-report inventories such as the Motivated Strategies for Learning Questionnaire (MSLQ) and the Self-Regulation Strategy Inventory―Cocky Report (SRSI-SR) measure SRL as a trait―and provide an aggregate assessment of SRL over multiple time points (Winne & Perry, 2000). However, a common criticism of these types of measures is that students may not remember their cerebral and metacognitive activities, and may not be able to admission or reflect upon them.

Withal, existing cocky-report inventories used to measure SRL strategies in college learners such as the Learning and Study Strategies Inventory (LASSI; Weinstein & Palmer, 1990) and the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & Mckeachie, 1993) measure SRL broadly. Other measures such as the Patterns of Adaptive Learning Scales (PALS; Midgley et al., 1996) and the Metacognitive Awareness Inventory (MAI; Schraw & Dennison, 1994) are used to measure specific subconstructs of SRL such as motivation and metacognition, respectively. The SRSI-SR too measures a subconstruct of SRL, merely differs in many ways from the aforementioned measures.

First, the SRSI-SR measures students' strategy use while studying for a content-specific task. This is an of import difference because information technology has been shown that SRL strategy apply tin can be subject and task specific (Cleary et al., 2012; Roth, Ogrin, & Schmitz, 2016). Second, while some measures of SRL do address maladaptive strategy apply in the form of negatively-worded questions (eastward.g., the MSLQ), the SRSI-SR devotes an unabridged subscale to maladaptive regulatory beliefs. Finally, the SRSI-SR was developed specifically for use with younger learners (main and secondary school students); many other measures of SRL are used primarily with older learners (post-secondary students).

The inventory, offset used by Cleary (2006) to measure out students' self-regulated learning strategies, includes three subscales: managing behavior and the surround, seeking data, and maladaptive regulatory behaviors.

Systematic literature reviews and meta-analyses have reported details regarding many of the most popular self-report measures of learning strategies and SRL (Credé & Phillips, 2011; Huang, 2011). Reviews of these types prove useful to researchers and practitioners alike, every bit they provide a synthesis of reliability, validity, and other psychometric information, and place strengths and limitations of the measure. However, in that location has still to exist a systematic review of the SRSI-SR. Thus, this work presents a systematic review of the SRSI-SR (Cleary, 2006), a context-specific self-report measure of self-regulated learning strategies. Specifically, we describe the academic subject areas in which the SRSI-SR has been administered, the age ranges examined, and other constructs with which the SRSI-SR has been compared. In add-on, available reliability information, validity evidence, and gaps in psychometric information for the instrument are likewise evaluated and discussed.

one.1. Theoretical Framework

Self-regulated students set goals, programme, enact strategies, are metacognitively aware, and evaluate their learning procedure for effectiveness and efficiency. Every bit Zimmerman (1986) describes, a student is self-regulated to the extent that they "are metacognitively, motivationally, and behaviorally active participants in their own learning process" (p. 308). Several SRL frameworks exist; some of the most common include Boekaerts's model of adjustable learning, Winne and Hadwin's four-stage model of SRL, Pintrich's typology of SRL, Borkowski'southward process-oriented model of SRL, and Zimmerman'due south three-stage model of SRL (Boekaerts, 1992; Borkowski & Muthukrishna, 1992; Pintrich, 2000; Winne & Hadwin, 1998; Zimmerman, 2000). Though models differ slightly, SRL is comprised of a preparatory/planning phase, continues through a performance phase, and includes an evaluation phase where appraisals and adaptations occur (Puustinen & Pulkkinen, 2001).

Zimmerman'southward (2008) model of SRL is of particular importance to the electric current review, every bit it provides theoretical foundation for the SRSI-SR. Zimmerman'south model, situated in social-cerebral theory, presents SRL as a three-phase circadian process, including forethought, performance, and self-reflection. In the forethought stage, students gear up learning goals, plan future actions, and select strategies to be used. In the operation phase, students implement strategies, focus attention to relevant stimuli, and detect their actions. During the self-reflection phase, and while completing the learning event, students create cocky-judgments about their learning and performance in situ too as self-reactions to those judgments (Erhan, 2016). The self-reflection phase also includes the actions (or non-actions) that students accept to maintain or alter their learning. A notable divergence among models is whether SRL is viewed as an consequence or as an aptitude (Winne & Perry, 2000).

Ane challenge in because SRL measures, therefore, is whether it is a state or trait. Viewing SRL as a trait, students' levels of SRL are believed to vary over long periods of time, between contexts, and between individuals. Nonetheless, others contend that SRL is characterized all-time as a state. That is, learners can effectively engage in SRL to differing degrees, depending on the context or demands of the chore (Winne & Perry, 2000). The assumption underlying the land-based view of SRL is that an observed SRL consequence has a specific kickoff and end (Winne & Perry, 2000). Still others debate that SRL is both a land and a trait (Hong, 1998; Schmidt, 2009). In light of the frequent use of trait-based measures of SRL (such as the SRSI-SR), systematic reviews of their employ are necessary in lodge to best inform futurity SRL research.

i.2. The Current Review

This work aimed to: 1) identify and examine studies that accept utilized the SRSI-SR to date; 2) analyze the psychometric backdrop of the SRSI-SR under unlike contexts and with varied-aged samples; and 3) describe relations among strategy use, every bit measured by the SRSI, and other constructs examined in the studies reviewed, iv) place gaps in current cognition about the measure out, and to offer future research recommendations based on findings.

2. Method

A brief description of the SRSI-SR, including example items for each subscale, is presented in Table one. A literature search was concluded in July of 2018. The search was performed using three mediums: Google Scholar, PsycINFO, and ProQuest. Google's citation index was used first to identify articles that cited Cleary's 2006 article. One hundred twenty-half-dozen such articles were identified; each was and so examined individually to determine appropriateness for the review. Studies were included in the review if they: 1) reported information from the SRSI-SR, ii) were written in English language, and 3) administered at least one of the three complete subscales. Studies were excluded from the review if they 1) did not report information from the SRSI-SR, ii) reported data only from either the Instructor Rating Scale or

Table 1. Descriptive tabular array of the SRSI-SR.

the Parent Rating Scale, or three) were not written in English. All publication dates were included.

PsycINFO and ProQuest were so separately searched using the post-obit search terms: self-regulation strategy inventory, Self-Regulation Strategy Inventory―Self-Report, SRSI, and SRSI-SR. Chiefly, no unique studies were obtained during these two searches. In addition to the to a higher place search strategies, personal contact was fabricated with colleagues known to have published with the inventory (n = 3) to place any current or ongoing studies utilizing the SRSI-SR or unpublished datasets that included SRSI-SR data. This process yielded one unpublished dataset. Based on the established inclusion and exclusion criteria, 18 studies were identified and included in subsequent analyses.

Each identified study was coded based upon the following characteristics: 1) Likert calibration blazon employed, two) blazon of written report conducted, (correlational or intervention), 3) student grade level, four) school discipline, and other constructs examined, such as motivation and achievement. Table two presents data near the SRSI-SR codes applied.

Some data were missing from three studies. Efforts were made to contact respective authors and gain access to their primary datasets to accost remaining questions about subscale or full-scale reliability, subscale mean values, and utilize of reverse coding. 2 of three studies' full datasets were obtained through these efforts. The third report, whose raw dataset could not be obtained, was included in analyses as appropriate.

Some studies used a 5-point scale instead of the original 7-point scale for students to report on the SRSI-SR. In these cases, a linear transformation was performed to convert the v-point scale to a 7-point scale so that meaningful comparisons could be fabricated across studies. Farther, intervention studies that used the SRSI-SR as a dependent variable often employed a pretest-posttest design, yielding 2 sets of scores for each participant. In these cases, only information from the first time-signal were considered for assay, every bit these are data virtually students' strategy use before any intervention occurred, and can therefore be compared more directly to other non-intervention studies.

Table 2. Codes practical for the SRSI-SR review.

3. Results

A summary of major findings from the review is presented in four sections. Offset, a descriptive overview of the samples, school field of study areas, and chronology of utilise is discussed. Second, available, tentative reliability evidence is presented. Third, available, tentative validity prove is presented. Finally, other constructs that take been measured concurrently with and their relations to the SRSI-SR are reviewed.

iv. Electric current Use

Table 3 presents the characteristics of studies included in the review. Participant characteristics varied widely across studies. As an example, the SRSI was used with student samples in which ninety% received complimentary or reduced lunch, and with samples where just near 6% of the schoolhouse received such aid. Further, the SRSI-SR was administered to language minority learners and native English speakers (Cleary, 2006; Cleary & Chen, 2009). Most studies (north = 15) were conducted in the United States, while some studies (Khodarahmi & Zarrinabadi, 2016; Madjar, Kaplan, & Weinstock, 2011; Madjar, Weinstock, & Kaplan, 2017) were conducted outside of the U.s.a. such every bit in Iran (Khodarahmi & Zarrinabadi, 2016) and Israel (Madjar et al., 2011, 2017). Additionally, sample sizes investigated ranged greatly in size, from four to 912. Researchers used the SRSI in various discipline areas ranging from English-language learning to environmental science settings. There are also Hebrew (Madjar et al., 2011) and Spanish versions (Cleary, 2006). Farther, studies that employed the SRSI also varied in terms of student class level. Most studies (north = 15) surveyed center or loftier schoolhouse students, though the overall range of samples included 5^th grade to graduate-level students. Interestingly, the SRSI-SR was used mainly in correlational studies and but a handful (due north = iii) of experimental or intervention-based studies used the instrument. Ten of the eighteen studies reviewed used the 5-point Likert scale as opposed to the original seven-point scale.

As indicated in Table 3, researchers' use of the SRSI-SR appears to be growing. In the past three years, for example, use of the SRSI-SR has doubled. This growth may be attributed to a number of factors, such as increased availability of validity and psychometric information well-nigh the instrument.

iv.1. Reliability Bear witness

Based on analysis of the studies reviewed, the SRSI-SR appears to show audio reliability as estimated by Cronbach's alpha (α ≥ 0.80). Reliability was also acceptable for the musical instrument at the subscale level. Estimates of reliability ranged from 0.82 to 0.92 amongst studies that used and reported the full inventory, with a median Cronbach'south alpha value of 0.91.

Factor 1―Managing environment and behavior. 13 studies reported reliability data for the first subscale. The reliability of scores on this subscale appeared, overall, to exist acceptable with median Cronbach's alpha values across the

Table iii. Characteristics of included studies.

Note: *indicates a negative relationship with the variable. **indicates a dissertation.

studies equally 0.87 (min = 0.66, max = 0.93). In 11 studies, reliability estimates met or exceeded 0.lxxx; two studies reported estimates of 0.66 and 0.69.

Gene 2―Seeking and learning data. Thirteen studies reported reliability data for the 2d subscale. The reliability of scores on this subscale appeared, overall, to also be adequate, with median Cronbach'southward blastoff of 0.80 (min = 0.71, max = 0.89). In eight of the studies, reliability estimates met or exceeded 0.80.

Gene 3―Maladaptive regulatory behavior. Fourteen studies reported reliability information for the third subscale. Reliability of scores was slightly lower on this subscale than others. That is, median Cronbach'southward alpha value across the studies was 0.76 (min = 0.64, max = 0.84) and the lowest reliability gauge was 0.64 for this subscale.

four.2. Validity Evidence

Cleary provided initial validity testify for the SRSI based upon internal structure and relations to other variables. Later on initial factor analysis of the instrument yielded a 3-factor solution, a second principle component analysis was conducted using the three SRSI-SR subscales and two measures of self-motivational beliefs, adult for the written report (Cleary, 2006). The three SRSI-SR subscales loaded onto one higher-order cistron and the two motivation scales loaded onto a separate higher-order cistron. This assay provided tentative validity evidence that the strategy-focused SRSI-SR is distinct from those item motivation scales.

Convergent validity evidence was nigh normally examined in the studies reviewed. Cleary & Chen (2009), for example, found that seventh-grade students who were classified as high-achievers exemplified more adaptive motivation and regulatory profiles across measures of interest and self-standards. That is, high achievers reported college involvement in math, college self-standards, and greater use of adaptive regulatory behaviors compared with their lower-achieving peers. Additionally, Follmer and Sperling (2016) reported SRSI-SR scores correlated significantly with the metacognitive self-regulation subscale of the MSLQ as well as scores on the Executive Skills Questionnaire (Dawson & Guare, 2010). Further, in a written report investigating SRL more broadly, Cleary, Dembitzer, and Kettler (2015) noted that the SRSI-SR exhibited statistically pregnant relations with four motivation measures and ii markers of regulation-related behaviors. Finally, Cleary, Platten, and Nelson (2008) implemented an intervention designed to increment SRL behaviors in students and establish significant differences from pretest to posttest in students' SRSI-SR scores.

4.3. The SRSI-SR as It Relates to Other Constructs

Other constructs measured in reviewed studies are presented in Table 3 and Tabular array 4. In Table 4, the "Miscellaneous" category included variables such as exam taking strategies, learning strategies, use of online learning tools, IQ scores, cocky-standards, and classroom environment. Considering "Motivation" was the most usually measured boosted construct (along with academic achievement or operation), and since there are many theories and dimensions of motivation, this construct was farther delineated (see Table 5). Achievement/performance

Table 4. Number of studies that measured other constructs by blazon of construct.

*Note: Non all constructs were included in all of the studies, thus the number of studies does not necessarily represent the number of r values that informed the median.

Table five. Number of studies that measured motivational constructs.

Note: Several studies employed measures of multiple motivation constructs.

was besides measured in various ways, almost frequently via test operation (v studies), course-grades (4 studies), or overall GPA (one written report). As expected, the SRSI relates positively to academic achievement and performance, overall (DiGiacomo, 2014; Hogrebe, 2015). Further, information technology relates negatively to such constructs as negative touch toward school, and the maladaptive regulatory beliefs subscale is positively correlated with performance-avoidance goal orientations (Madjar et al., 2011), and negatively correlated with math operation and the examination-taking skills subscale of the LASSI (Cleary & Callan, 2014). Table 4 reports median Pearson's r correlations with the SRSI-SR, by construct. Generally, the correlations between the SRSI-SR and other constructs are in the expected direction and are of moderate forcefulness.

5. Word

Despite the variety of academic subjects, age groups, SES, and other constructs with which the SRSI-SR accept been measured, a more comprehensive understanding of the instrument is needed. The current work helps elucidate areas in need of further investigation. First, though most estimates of reliability were adequate on the subscale level, some were notably low. The first subscale had a minimum estimate of 0.66 (Madjar et al., 2011). 1 potential reason for this unexpectedly low estimate is the linguistic communication in which the measure was administered. The study that reported this value used the Hebrew version of the mensurate, which may have negatively impacted reliability. Additionally, the tertiary subscale had a minimum alpha value of 0.64 (Delen, Liew, & Willson, 2014). While the authors of this written report did utilize the English version, they also pooled both undergraduate and graduate-level participants into one sample. The differences betwixt these populations of learners may take negatively affected the reliability estimate of this subscale.

While the reported internal consistency estimates were adequate in most studies, little information about the stability of SRSI-SR scores is available. Stability of scores is important because the SRSI measures SRL every bit a trait, and as such, should exist relatively stable over fourth dimension (Winne & Perry, 2000). In two intervention studies (Cleary & Platten, 2013; Cleary et al., 2008) reliability modify alphabetize (RCI) values were reported, nonetheless, the two administrations of the SRSI-SR in both studies were separated by an intervention for all participants. Of the 12 RCI values reported, (four participants, three subscales; Cleary & Platten, 2013), i value reached significance (RCI = −ii.00, p < .05). This value corresponded to the maladaptive regulatory behavior subscale, and indicated that the observed change in scores on this subscale for this participant was not due to random fluctuations in the mensurate (unreliability). On the other hand, Cleary et al. (2008) reported significant RCI values for all three subscales. Since both of these studies were interventions, the stability of the instrument remains unknown.

Further, though many studies offered convergent validity evidence, just one written report (Cleary, 2006) presented both convergent and discriminant validity show. More work needs to be done to ensure adequate evidence of validity for the SRSI-SR. First, more large-sample studies are needed to conduct further factor analytic work to approve the gene structure of the SRSI. Without an authentic factor construction, the mensurate's subscale reliabilities may exist negatively afflicted. 2nd, futurity research should investigate how the SRSI-SR relates to other closely-related constructs such as metacognition. 3rd, the SRSI-SR should be included in more studies using multiple methods of SRL measurement, such as event sampling measures, and trace information. Such additional studies will provide further validity show regarding the SRSI-SR, and volition provide information for which the common criticisms of self-report measures can be addressed.

Equally SRL is important during and afterwards the transition from high school to higher, audio measures are needed for these students. All the same, the relative lack of studies administering the SRSI-SR to post-secondary students limits our psychometric knowledge about the measure with this population. Thus, the psychometric backdrop of the SRSI-SR need farther investigation with mail service-secondary students. Currently, only v studies used the SRSI-SR with students ranging from 11^thursday grade through undergraduate. Further, apply of the SRSI has been limited amongst some undergraduate contexts, such as Stem disciplines. With increasing accent placed on Stalk education (Benson et al., 2016; Olson & Riordan, 2012), and the high potential for growth in Stalk career fields over the adjacent few years (Carpi, Ronan, Falconer, & Lents, 2017; Casey, 2012), audio measures of SRL strategy use will be needed for research in these areas. Thus, time to come research that investigates SRL strategy utilise in Stalk subjects should consider using the SRSI-SR, to see if information technology performs adequately in these contexts. Finally, some other artery of future work could investigate the psychometric backdrop of the SRSI-SR when used with non-typically developing students, such equally those students with learning disabilities. Only ane study has thus far undertaken this task (Gelbar, Bray, Kehle, Madaus, & Makel, 2016), so future research should build from these initial findings.

Overall, the SRSI-SR is emerging as a audio measure of SRL strategy use. Considering its relatively short length compared to other measures of SRL strategy use, and its relative ease of assistants, the instrument could be quite useful for practitioners wishing to assess their students' strategy use. Despite the measure's promising convergent validity evidence, discriminant validity bear witness is still lacking. Additionally, the measure'due south internal construction needs to be confirmed, and time to come piece of work should also include the SRSI as office of a battery of SRL measures that likewise includes non-self-report methods.

Conflicts of Involvement

The authors declare no conflicts of interest.

References

[1]	Benson, L. C., Kennedy, M. South., Ehlert, K. M., Vargas, P. Yard. D., Faber, C. J., Kajfez, R. L., & McAslister, A. K. (2016). Agreement Undergraduate Engineering Researchers and How They Learn. In 2016 IEEE Frontiers in Instruction Conference (FIE) (pp. i-five). Erie, PA: Institute of Electronics and Electrical Engineers. https://doi.org/10.1109/FIE.2016.7757630
[2]	Boekaerts, G. (1992). The Adaptable Learning Procedure: Initiating and Maintaining Behavioural Change. Practical Psychology, 41, 377-397. https://doi.org/ten.1111/j.1464-0597.1992.tb00713.x
[iii]	Borkowski, J., & Muthukrishna, North. (1992). Moving Metacognition into the Classroom: "Working Models" and Effective Strategy Teaching. In Grand. Pressley, K. R. Harris, & J. T. Guthrie (Eds.), Promoting Bookish Literacy: Cognitive Research and Instructional Innovation (pp. 477-501). Orlando, FL: Academic Press.
[4]	Callan, G. L., & Cleary, T. J. (2018). Multidimensional Assessment of Cocky-Regulated Learning with Middle School Math Students. School Psychology Quarterly, 33, 103-111. https://doi.org/ten.1037/spq0000198
[five]	Carpi, A., Ronan, D. M., Falconer, H. M., & Lents, Due north. H. (2017). Cultivating Minority Scientists: Undergraduate Inquiry Increases Self-Efficacy and Career Ambitions for Underrepresented Students in STEM. Periodical of Inquiry in Science Teaching, 54, 169-194. https://doi.org/10.1002/tea.21341
[six]	Casey, B. (2012). STEM Educational activity: Preparing for the Jobs of the Hereafter. US Congress Articulation Economical Committee, Washington DC.
[7]	Cleary, T. J. (2006). The Evolution and Validation of the Self-Regulation Strategy Inventory-Self-Report. Journal of Schoolhouse Psychology, 44, 307-322. https://doi.org/10.1016/j.jsp.2006.05.002
[8]	Cleary, T. J. (2011). Emergence of Self-Regulated Learning Microanalysis: Historical Overview, Essential Features, and Implications for Research and Practice. In B. J. Zimmerman, & D. H. Schunk (Eds.), Handbook of Self-Regulation of Learning and Performance (pp. 329-345). New York, NY: Routledge.
[9]	Cleary, T. J., & Callan, G. L. (2013). Pupil Self-Regulated Learning in an Urban High Schoolhouse: Predictive Validity and Relations Between Instructor Ratings and Student Cocky-Reports. Journal of Psychoeducational Cess, 32, 295-305.
[10]	Cleary, T. J., & Chen, P. P. (2009). Self-Regulation, Motivation, and Math Achievement in Middle School: Variations across Course Level and Math Context. Periodical of Schoolhouse Psychology, 47, 291-314. https://doi.org/10.1016/j.jsp.2009.04.002
[eleven]	Cleary, T. J., & Platten, P. (2013). Examining the Correspondence between Cocky-Regulated Learning and Academic Accomplishment: A Case Written report Analysis. Pedagogy Research International, 2013, Article ID: 272560.
[12]	Cleary, T. J., Callan, G. Fifty., & Zimmerman, B. J. (2012). Assessing Self-Regulation equally a Cyclical, Context-Specific Phenomenon: Overview and Analysis of SRL Microanalytic Protocols. Pedagogy Research International, 2012, Commodity ID 428639. https://doi.org/x.1155/2012/428639
[13]	Cleary, T. J., Dembitzer, L., & Kettler, R. J. (2015). Internal Factor Structure and Convergent Validity Evidence: The Self-Report Version of the Cocky-Regulation Strategy Inventory. Psychology in the Schools, 52, 829-844. https://doi.org/10.1002/pits.21866
[14]	Cleary, T. J., Platten, P., & Nelson, A. (2008). Effectiveness of the Self-Regulation Empowerment Program with Urban High Schoolhouse Students. Journal of Avant-garde Academics, 20, 70-107. https://doi.org/10.4219/jaa-2008-866
[15]	Credé, M., & Phillips, 50. A. (2011). A Meta-Analytic Review of the Motivated Strategies for Learning Questionnaire. Learning and Individual Differences, 21, 337-346. https://doi.org/10.1016/j.lindif.2011.03.002
[16]	Dawson, P., & Guare, R. (2010). Executive Skills in Children and Adolescents: A Applied Guide to Assessment and Intervention (second ed.). New York, NY: The Guilford Press.
[17]	Delen, E., Liew, J., & Willson, V. (2014). Furnishings of Interactivity and Instructional Scaffolding on Learning: Self-Regulation in Online Video-Based Environments. Computers and Didactics, 78, 312-320. https://doi.org/10.1016/j.compedu.2014.06.018
[18]	DiGiacomo, One thousand. (2014). Enhancing Cocky-Monitoring and Cocky-Reflection through a Cocky-Regulatory Skills Intervention Embedded in a Eye Schoolhouse Mathematics Curriculum. ProQuest Dissertations and Theses. http://libservy.nie.edu.sg/login.php/?url=http://search.proquest.com/docview/ 1552722651?accountid=28158%5Cnhttp://hf9lf4zb8t.search.serialssoluti ons.com/?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&rfr_id=info:sid/ProQuest+Dissertations+&+Theses+A&I&rf
[19]	Erhan, One thousand. Chiliad. (2016). An Overview of Self-Regulated Learning Models. Baskent University Periodical of Education, iii, 124-131.
[twenty]	Follmer, D. J., & Sperling, R. A. (2016). The Mediating Role of Metacognition in the Human relationship betwixt Executive Function and Self-Regulated Learning. British Journal of Educational Psychology, 86, 559-575. https://doi.org/ten.1111/bjep.12123
[21]	Follmer, D. J., Sperling, R. A., & Hu, H. (2016). Employing a Multidimensional Cess Arroyo: An Test of Relations among Measures of Self-Regulated Learning. Presented at the Annual Coming together of the American Educational Research Association, Washington DC.
[22]	Gelbar, Due north. W., Bray, Yard., Kehle, T. J., Madaus, J. W., & Makel, C. (2016). Exploring the Nature of Compensation Strategies in Individuals with Dyslexia. Canadian Journal of Schoolhouse Psychology, 33, 110-124.
[23]	Hogrebe, J. Thou. (2015). Self-Regulated Learning and Motivation Belief Differences among Gifted and Not-Gifted Middle Schoolhouse Students across Accomplishment Levels. Doctoral Dissertation.
[24]	Hong, Eastward. (1998). Differential Stability of State and Trait Cocky-Regulation in Academic Operation. The Journal of Educational Research, 91, 148-158. https://doi.org/10.1080/00220679809597536
[25]	Huang, C. (2011). Achievement Goals and Achievement Emotions: A Meta-Assay. Educational Psychology Review, 23, 359-388. https://doi.org/10.1007/s10648-011-9155-x
[26]	Khodarahmi, Eastward., & Zarrinabadi, N. (2016). Self-Regulation and Bookish Optimism in a Sample of Iranian Language Learners: Variations beyond Accomplishment Grouping and Gender. Current Psychology, 35, 700-710. https://doi.org/x.1007/s12144-015-9340-z
[27]	Lubin, A. R. (2015). Validation of the Self-Regulation Strategy Inventory: Parent Rating Scale.
[28]	Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying Mastery-Avoidance Goals in High Schoolhouse: Distinguishing between Intrapersonal and Chore-Based Standards of Competence. Contemporary Educational Psychology, 36, 268-279. https://doi.org/10.1016/j.cedpsych.2011.03.003
[29]	Madjar, N., Weinstock, M., & Kaplan, A. (2017). Epistemic Beliefs and Achievement Goal Orientations: Relations between Constructs versus Personal Profiles. The Journal of Educational Research, 110, 32-49. https://doi.org/ten.1080/00220671.2015.1034353
[30]	Midgley, C., Maehr, M. L., Hicks, L., Roeser, R., Urdan, T., Anderman, East., Middleton, Thousand. et al. (1996). Patterns of Adaptive Learning Survey (PALS). Ann Arbor, MI: Center for Leadership and Learning.
[31]	Muis, K. R., Winne, P. H., & Jamieson-Noel, D. (2007). Using a Multitrait-Multimethod Analysis to Examine Conceptual Similarities of Three Self-Regulated Learning Inventories. British Journal of Educational Psychology, 77, 177-195. https://doi.org/10.1348/000709905X90876
[32]	Nelson, J. A. (2014). Self-Regulated Learning, Classroom Context, and Achievement: A Dual-Method Investigation.
[33]	Olson, Due south., & Riordan, D. Grand. (2012). Engage to Excel: Producing One Million Boosted Higher Graduates with Degrees in Scientific discipline, Applied science, Applied science, and Mathematics. Report to the President, Executive Office of the President.
[34]	Pintrich, P. R. (2000). The Role of Goal Orientation in Cocky-Regulated Learning. In M. Boekaerts, P. R. Pintrich, & G. Zeidner (Eds.), Handbook of Self-Regulation (pp. 451-502). San Diego, CA: Bookish Press.
[35]	Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, Westward. J. (1993). Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53, 801-813. https://doi.org/10.1177/0013164493053003024
[36]	Puustinen, K., & Pulkkinen, L. (2001). Models of Self-Regulated Learning: A Review. Scandinavian Periodical of Educational Research, 45, 269-286. https://doi.org/x.1080/00313830120074206
[37]	Roth, A., Ogrin, S., & Schmitz, B. (2016). Assessing Cocky-Regulated Learning in Higher Education: A Systematic Literature Review of Self-Report Instruments (pp. 225-250).
[38]	Schmidt, M. (2009). How to Manage Your PhD Thesis: Development of a Procedure Model of Self-Regulation to Foster Postgraduate Students. Hamburg: Kovac.
[39]	Schmitz, B., Klug, J., & Schmidt, M. (2011). Assessing Self-Regulated Learning Using Diary Measures with University Students. In B. J. Zimmerman, & D. H. Schunk (Eds.), Handbook of Cocky-Regulation of Learning and Performance (pp. 251-266). New York: Routledge.
[40]	Schraw, Yard., & Dennison, R. Southward. (1994). Assessing Metacognitive Awareness. Contemporary Educational Psychology, 19, 460-475. https://doi.org/10.1006/ceps.1994.1033
[41]	Turner, J. C. (1995). The Influence of Classroom Contexts on Immature Children's Motivation for Literacy. Reading Research Quarterly, 30, 410-441. https://doi.org/10.2307/747624
[42]	Weinstein, C. Eastward., & Palmer, D. R. (1990). LASSI-HS: Learning and Study Strategies Inventory-High School Version. H & H Publishing Visitor.
[43]	Winne, P. H., & Hadwin, A. (1998). Studying as Cocky-Regulated Learning. In D. J. Hacker, & J. Dunlosky (Eds.), Metacognition in Educational Theory and Practise (pp. 277-304). Hillsdale, NJ: Lawrence Erlbaum.
[44]	Winne, P. H., & Perry, N. E. (2000). Measuring Self-Regulated Learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 531-566). Cambridge: Academic Press. https://doi.org/10.1016/B978-012109890-2/50045-7
[45]	Winne, P. H., Hadwin, A. F., Stockley, D. B., & Nesbit, J. C. (1997). Traces versus Self-Reports of Report Tactics and Their Relations to Achievement. Annual Meeting of the American Educational Research Association, Chicago, IL.
[46]	Zimmerman, B. J. (1986). Becoming a Cocky-Regulated Learner: Which Are the Key Subprocesses? Contemporary Educational Psychology, 11, 307-313.
[47]	Zimmerman, B. J. (1990). Self-Regulated Learning and Bookish Accomplishment: An Overview. Educational Psychologist, 25, 3-17. https://doi.org/x.1207/s15326985ep2501_2
[48]	Zimmerman, B. J. (2000). Attaining Self-Regulation: A Social Cognitive Perspective. In M. Boekaerts, P. R. Pintrich, & 1000. Zeidner (Eds.), Handbook of Self-Regulation (pp. 13-39). San Diego, CA: Academic Press.
[49]	Zimmerman, B. J. (2008). Investigating Self-Regulation and Motivation: Historical Groundwork, Methodological Developments, and Future Prospects. American Educational Research Journal, 45, 166-183. https://doi.org/10.3102/0002831207312909

ayresshmis1937.blogspot.com

Source: https://www.scirp.org/journal/paperinformation.aspx?paperid=90647

Share this post