Search

Metacognition is the engine of self-regulated learning. At the object level, learners seek information and choose learning tactics and strategies they forecast will develop knowledge. At the meta level, learners gather and analyze data about learning events to draw conclusions, such as: Is this tactic a good fit to conditions? Was it effective? Was effort required reasonable? Is my ability publicly exposed? As data accumulate, learners shape, re-shape and refine a personal theory about optimal learning. Thus, self-regulating learners are learning scientists. However, without training and tools on which "professional" learning scientists rely, learners' N = me research programs are naïve and scruffy. Merging models of tasks, cognition, metacognition and motivation, I describe software tools, approaches to analyzing data and learning analytics designed to serve three goals: supporting self-regulating learners' metacognition in N = me research, accelerating professional learning scientists' research, and boosting synergy among learners and learning scientists to accelerate progress in learning science.

This special issue presents a sample of modern work on self-regulated learning (SRL) among high ability and gifted students. It includes diverse views about the construct per se, and gifted students' and their teachers' accounts about SRL and factors they believe moderate it. Zeidner and Stroeger (this issue) set the stage with a sketch of an extensive literature about SRL that has deep roots in North American educational philosophy and practice. The menu of work here is fundamentally well done and, in varying ways and degrees, slightly provocative. A trite observation would be these articles don't fully represent the multiple facets and complex articulation among them comprising SRL, especially given relatively less research with participants identified as academically talented or gifted. In this situation, I would be pedantic to point out such-and-such is omitted or this-or-that is underrepresented. Rather, using admittedly using idiosyncratic standards, I select a few matters for discussion and, hopefully, constructive critique. Other commentators would likely apply different filters.

Our article introduces the "Journal of Educational Data Mining's" Special Issue on Educational Data Mining on Motivation, Metacognition, and Self-Regulated Learning. We outline general research challenges for data mining researchers who conduct investigations in these areas, the potential of EDM to advance research in this area, and issues in validating findings generated by EDM.

Self-explanation is a process by which learners generate inferences about causal connections or conceptual relationships. A meta-analysis was conducted on research that investigated learning outcomes for participants who received self-explanation prompts while studying or solving problems. Our systematic search of relevant bibliographic databases identified 69 effect sizes (from 64 research reports) which met certain inclusion criteria. The overall weighted mean effect size using a random effects model was g = 0.55. We coded and analyzed 20 moderator variables including type of learning task (e.g., solving problems, studying worked problems, and studying text), subject area, level of education, type of inducement, and treatment duration. We found that self-explanation prompts are a potentially powerful intervention across a range of instructional conditions. Due to the limitations of relying on instructor-scripted prompts, we recommend that future research explore computer-generation of self-explanation prompts.

New methods for gathering and analyzing data about events that comprise self-regulated learning (SRL) support discoveries about patterns among events and tests of hypotheses about roles patterns play in learning. Five such methodologies are discussed in the context of four key questions that shape investigations into patterns in SRL. A framework for this review is provided by a model that structures SRL in terms of: conditions of a task, operations, products generated by operations, evaluations of work and standards used in evaluations (COPES; Winne in "Journal of Educational Psychology, 89", 397-410, 1997). Four recommendations are made for future work on SRL as patterned activity: prune models of SRL with experimental tests, explicitly include goals in data, ensure learners have options for SRL by training them in tactics and strategies, and provide learners access to accurate displays about the events and patterns that comprise SRL.

School tasks interact with student motivation, cognition, and instruction to influence learning and achievement. Heeding calls for additional research linking motivational and cognitive factors in learning and instruction on specific tasks within authentic classroom settings we quantitatively and qualitatively track 90 tenth-grade science students' motivation, reported use of learning strategies, achievement, calibration, and task perceptions as they engage in a well-structured task (WST) and an ill-structured task (IST). Students achieved higher grades on, and reported more ease and value for, the WST whereas they utilised critical thinking and peer learning strategies more on the IST. Lower academic achievers calibrated their achievement less accurately on each task and experienced lower grades, interest, ease, and management capability on the IST. Conversely, higher academic achieving students reported more self-efficacy and effort regulation and lower anxiety and elaboration on the IST. Motivation--notably less intrinsic goal orientation in low academic achievers and higher task value and self-efficacy--predicted performance on the IST. The structure of tasks may provide prompts that illicit unique self-regulated learning responses in students. (Contains 4 tables and 1 figure.)

Links between students' achievement goal orientations and learning tactics were investigated using software (gStudy) that supports a variety of learning tactics and strategies. An achievement goal questionnaire was administered to 307 students enrolled in an introductory educational psychology course. Data tracing study tactics were logged for 80 of these students who prepared for a test by studying a textbook chapter presented as a multimedia document. Using correlations and canonical correlations, we found relationships between goal orientations and activity traces indicating different forms of cognitive engagement. Notably, mastery goal orientation (approach or avoidance) was negatively related to amount of highlighting, a study tactic that is theorized to be less effective than summarizing and other forms of elaborative annotation for assembling and integrating knowledge. (Contains 5 tables.)

The trajectory of scholarship about self-regulated learning (SRL) originates in mid-19th-century writings about learners' sense of responsibility in self education. Although Descartes's 17th-century writings implied mental activities consistent with metacognition, a central feature of SRL, these were inarticulate until Flavell and colleagues' studies circa 1970. Since then, research on metacognition and its role in SRL has approximately doubled every decade. Foundations for modeling SRL include Skinner's behaviorism, which acknowledged learners' choices about reinforcers for behavior, and Bandura's social learning theory, with its construct of agency. Research in the 1980s gathered data about SRL mainly using interviews, self-report questionnaires, and think-aloud protocols. These methods were quickly supplemented by observations of behavior and traces of learning activities tightly coupled to features of SRL. Today, SRL research is prominent across a broad spectrum of educational topics. Its importance will grow with trends toward lifelong learning and self-directed inquiries that survey vast information on the Internet, where students control what and how they will learn. Implications for future research include reconceptualizing "error variance" as arising partially due to SRL and capitalizing on software technologies that massively increase access to data about how and to what effects learners self-regulate learning.

Models of self-regulated learning hypothesize that learners selectively match study tactics to varying tasks and diverse goals. In this study, students rated the frequency with which they applied 26 study tactics, used 20 textbook features and other resources, and adopted 30 goals for studying. Findings indicate students' reports of self-regulating studying behaviors are context specific. (Author)

Advances a view of instruction based on the nature of cognitive activity, school learning, and instruction and suggests relationships between the CEDARS, cognitive mediational, and task models of instruction. Discusses more than a dozen implications of the cognitive mediational and task models for ways teachers can promote students' cognitive achievements. (DT)

A study involving 199 eighth graders in British Columbia assessed the use of conceptual, analogical, and outline methods of organization of textual material concerning computer crime and prevention. Findings indicate that simple presentation of a genuine advance organizer does not guarantee that students will use it effectively. (TJH)

This document discusses the use of Information Communication Technology (ICT) in education, particularly in the context of blended or online learning environments. It highlights the need for educational institutions to analyze the data available through learning management systems (LMSs) and use it to improve teaching and learning. The document includes contributions from various authors and institutions across different countries, providing insights into how ICT is currently being used in education. It covers topics such as artificial intelligence, applied robotics, automated approaches to create smart and inclusive learning environments, emerging technologies, learning analytics, and learning strategies for smart learning environments. The document also presents recommendations for future research in the field of ICT in education. [Extracted from the article]

Metacognition is the engine of self-regulated learning. At the object level, learners seek information and choose learning tactics and strategies they forecast will develop knowledge. At the meta level, learners gather and analyze data about learning events to draw conclusions, such as: Is this tactic a good fit to conditions? Was it effective? Was effort required reasonable? Is my ability publicly exposed? As data accumulate, learners shape, re-shape and refine a personal theory about optimal learning. Thus, self-regulating learners are learning scientists. However, without training and tools on which "professional" learning scientists rely, learners' N = me research programs are naïve and scruffy. Merging models of tasks, cognition, metacognition and motivation, I describe software tools, approaches to analyzing data and learning analytics designed to serve three goals: supporting self-regulating learners' metacognition in N = me research, accelerating professional learning scientists' research, and boosting synergy among learners and learning scientists to accelerate progress in learning science. [ABSTRACT FROM AUTHOR]

Background: Guidance during inquiry learning plays an important role in developing conceptual understanding and inquiry skills. This study analysed learner‐tutor interactions in a simulation‐assisted learning environment to investigate how tutor guidance enabled knowledge construction and fostered epistemic practice. Objectives: This research aimed to illuminate challenges learners encounter in the inquiry process and forms of guidance that support learning in both conceptual and epistemic aspects. Methods: This study uses a mixed methods approach. We analysed video recordings in which nine participants asked 72 questions and the microsequences of interactions immediately surrounding and including each question. We coded properties of each question and whether the tutors' utterances were intended to increase (upregulate) or decrease (downregulate) the complexity of the inquiry processes, and used a two‐step cluster analysis to explore groupings emerged from tutors' regulation guidance and learners' questions. Results and Conclusions: The regulatory intent of tutors' utterances depended on various characteristics of student questions. The microsequences clustered in five categories: 1) upregulated investigation and inference, 2) upregulated evidence‐based justification, 3) downregulated cognitive load, 4) downregulated procedural uncertainties, and 5) downregulated perceptual dissonance. Our findings suggest tutors offering guiding prompts should consider dual processes in the inquiry and, by strategically prompting them, strike a balance between the goals of guiding learners to discover scientific knowledge and grounding their conceptual understanding in concepts, data, and procedures. Implications: We emphasize conceptual and epistemic learning should be concurrently guided in scientific inquiry. We propose a bidirectional guidance model as a pedagogical approach to guide instructional practice. Lay Description: What is currently known about the subject matter: Rapid technological change urges schools to equip students with research and inquiry skills.Computer simulations provide an information‐rich environment for inquiry‐based learning.Guidance in inquiry learning is important for knowledge acquisition and the development of inquiry skills. What this paper adds: We illuminated challenges learners encounter in the inquiry learning process.We analysed forms of guidance that support learning in both conceptual and epistemic aspects.We identified dual processes involved in the inquiry process that have important implications.We proposed a bidirectional model for adaptive guidance in scientific inquiry. Implications of study findings for practitioners: Adaptive guidance facilitates conceptual and epistemic understanding by stimulating curiosity and reducing uncertainty in the inquiry process.Conceptual and epistemic learning need to be concurrently guided in scientific inquiry. [ABSTRACT FROM AUTHOR]

Studies described in this special issue take bold steps in using diverse instrumentation to gather multidimensional data about self-regulated learning, and apply novel analytic methods to examine those data. I explore these advances from a perspective that foregrounds the role of a paradigm – coherent theoretical propositions and methodological properties – that underlie and contextualize the research. Several suggestions are offered for consideration about interpreting findings and designing future research. [ABSTRACT FROM AUTHOR]

The trajectory of scholarship about self-regulated learning (SRL) originates in mid-19th-century writings about learners' sense of responsibility in self education. Although Descartes's 17th-century writings implied mental activities consistent with metacognition, a central feature of SRL, these were inarticulate until Flavell and colleagues' studies circa 1970. Since then, research on metacognition and its role in SRL has approximately doubled every decade. Foundations for modeling SRL include Skinner's behaviorism, which acknowledged learners' choices about reinforcers for behavior, and Bandura's social learning theory, with its construct of agency. Research in the 1980s gathered data about SRL mainly using interviews, self-report questionnaires, and think-aloud protocols. These methods were quickly supplemented by observations of behavior and traces of learning activities tightly coupled to features of SRL. Today, SRL research is prominent across a broad spectrum of educational topics. Its importance will grow with trends toward lifelong learning and self-directed inquiries that survey vast information on the Internet, where students control what and how they will learn. Implications for future research include reconceptualizing "error variance" as arising partially due to SRL and capitalizing on software technologies that massively increase access to data about how and to what effects learners self-regulate learning. [ABSTRACT FROM AUTHOR]

This exploratory case study examined in depth the studying activities of eight students across two studying episodes, and compared traces of actual studying activities to self-reports of self-regulated learning. Students participated in a 2-hour activity using our gStudy software to complete a course assignment. We used log file data to construct profiles of self-regulated learning activity in four ways: (a) frequency of studying events, (b) patterns of studying activity, (c) timing and sequencing of events, and (d) content analyses of students' notes and summaries. Findings indicate that students' self-reports may not calibrate to actual studying activity. Analyses of log file traces of studying activities provide important information for defining strategies and sequences of fine-grained studying actions. We contrast these analytic methods and illustrate how trace-based profiles of students' self-regulated studying inform models of metacognitive monitoring, evaluation, and self-regulated adaptation. [ABSTRACT FROM AUTHOR]

Links between students' achievement goal orientations and learning tactics were investigated using software (gStudy) that supports a variety of learning tactics and strategies. An achievement goal questionnaire was administered to 307 students enrolled in an introductory educational psychology course. Data tracing study tactics were logged for 80 of these students who prepared for a test by studying a textbook chapter presented as a multimedia document. Using correlations and canonical correlations, we found relationships between goal orientations and activity traces indicating different forms of cognitive engagement. Notably, mastery goal orientation (approach or avoidance) was negatively related to amount of highlighting, a study tactic that is theorized to be less effective than summarizing and other forms of elaborative annotation for assembling and integrating knowledge. [ABSTRACT FROM AUTHOR]

Examines how student develop forms for self-regulated learning. Implications of self-regulated learning in educational psychology; Details on self-regulated learning; Details on how student develop their own learning skills.

This article analyzes the concept of validity to set out key factors bearing on claims about validity in general and particularly regarding learning analytics. Because uses of trace data in learning analytics are increasing rapidly, specific consideration is given to reliability of trace data and their role in claiming validity for interpretations grounded on trace data. This analysis reveals the essential and inescapable role of theory in deciding what trace data should be gathered and how trace data can contribute to recommendations for improving learning, one main goal for generating and using learning analytics. • Trace data are increasingly useful in developing learning analytics. • "Raw" data are biased by the theory that recommends observing those data. • Self-regulating learners acting as agents complicate reliability of trace data. • Reliability of trace data concerns dynamic events, not static aspects of a measure. • Generalizability over facets of data sets limits on reliability and validity. [ABSTRACT FROM AUTHOR]