This article describes the ICAP framework that defines cognitive engagement activities on the basis of students’ overt behaviors and proposes that engagement behaviors can be categorized and differentiated into one of four modes: Interactive, Constructive, Active, and Passive. The ICAP hypothesis predicts that as students become more engaged with the learning materials, from passive to active to constructive to interactive, their learning will increase. We suggest possible knowledge-change processes that support the ICAP hypothesis and address the limitations and caveats of the hypothesis. In addition, empirical validation for the hypothesis is provided by examining laboratory and classroom studies that focus on three specific engagement activities: note taking, concept mapping and self-explaining. We also consider how ICAP can be used as a tool for explaining discrepant findings, dictate the proper choice of a control condition, and evaluate students’ outputs. Finally, we briefly compare ICAP to existing theories of learning.
This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.
We tested key predictions of a theoretical model positing that confusion, which accompanies a state of cognitive disequilibrium that is triggered by contradictions, conflicts, anomalies, erroneous information, and other discrepant events, can be beneficial to learning if appropriately induced, regulated, and resolved. Hypotheses of the model were tested in two experiments where learners engaged in trialogues on scientific reasoning concepts in a simulated collaborative learning session with animated agents playing the role of a tutor and a peer student. Confusion was experimentally induced via a contradictory-information manipulation involving the animated agents expressing incorrect and/or contradictory opinions and asking the (human) learners to decide which opinion had more scientific merit. The results indicated that self-reports of confusion were largely insensitive to the manipulations. However, confusion was manifested by more objective measures that inferred confusion on the basis of learners’ responses immediately following contradictions. Furthermore, whereas the contradictions had no effect on learning when learners were not confused by the manipulations, performance on multiple-choice posttests and on transfer tests was substantially higher when the contradictions were successful in confusing learners. Theoretical and applied implications are discussed.
In recent years, two communities have grown around a joint interest on how big data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of educational data mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.