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Capturing evidence for dynamic changes in self‐regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments. Practitioner notesWhat is already known about this topic Self‐regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.SRL is a dynamic, temporal process that leads to purposeful student engagement.Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.What this paper adds A Markov process for measuring dynamic SRL processes using log data.Evidence that dynamic, interaction‐dominant aspects of SRL predict student achievement.Evidence that SRL processes can be meaningfully impacted through educational intervention.Implications for theory and practice Complexity approaches inform theory and measurement of dynamic SRL processes.Static representations of dynamic SRL processes are promising learning analytics metrics.Engineered features of LMS usage are valuable contributions to AI models. [ABSTRACT FROM AUTHOR]

Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for meaningful learning across disciplines, teachers are required to assess their students' conceptions. The focus of this article lies on the discussion of learning analytics for supporting the assessment of students' conceptions in class. The existing and potential contributions of learning analytics are discussed related to the named formative assessment framework in order to enhance the teachers' options to consider individual students' conceptions. We refer to findings from biology and computer science education on existing assessment tools and identify limitations and potentials with respect to the assessment of students' conceptions. Practitioner notesWhat is already known about this topic Students' conceptions are considered to be important for learning processes, but interpreting evidence for learning with respect to students' conceptions is challenging for teachers.Assessment tools have been developed in different educational domains for teaching practice.Techniques from artificial intelligence and machine learning have been applied for automated assessment of specific aspects of learning.What does the paper add Findings on existing assessment tools from two educational domains are summarised and limitations with respect to assessment of students' conceptions are identified.Relevent data that needs to be analysed for insights into students' conceptions is identified from an educational perspective.Potential contributions of learning analytics to support the challenging task to elicit students' conceptions are discussed.Implications for practice and/or policy Learning analytics can enhance the eliciting of students' conceptions.Based on the analysis of existing works, further exploration and developments of analysis techniques for unstructured text and multimodal data are desirable to support the eliciting of students' conceptions. [ABSTRACT FROM AUTHOR]

This paper explores the effect of digital differentiation on student performance using a randomized experiment. The experiment is conducted in a second year biology class among 115 prevocational students in the Netherlands. Differentiation allowed students in the treatment group to work at three different levels. The results show that there is a significant effect of digital differentiation on the posttest score. This effect is robust to adding covariates such as students' ability, grade repetition, age, gender, class and average neighborhood income. There are no differential effects when dividing students in three groups, by ability. The results imply that differentiation in large classrooms is possible and beneficial for all students, once done digitally. [ABSTRACT FROM AUTHOR]

From the lens of Self‐Determination Theory, this study investigated the effects of a mobile application tool for identifying species on biology students' achievement and well‐being. It was hypothesized that the mobile application, compared to a textbook, would enhance feelings of competence and autonomy and, in turn, intrinsic motivation, positive affect and achievement, because the mobile application's built‐in functions provide students with choice and volition, informational feedback, and optimal challenges. Fifty‐eight second‐year students were randomly assigned to use either the mobile application or a textbook for a learning task. Well‐being was assessed before and after the learning task, and intrinsic motivation, perceived competence, perceived autonomy and achievement were assessed after the task. Results indicated that the mobile application, relative to the textbook, produced higher levels of students' perceived competence, perceived autonomy and intrinsic motivation. Further, the mobile application had indirect effects on positive affect through autonomy, competence and intrinsic motivation, and on achievement through competence. [ABSTRACT FROM AUTHOR]

Compares the Web-based, correspondence and lecture versions of a second-year non-major biology course. Students' satisfaction with the Web version; Comparison of the mean scores of correspondence and Web-based courses through several semesters; Benefits and limitations of both Web-based and correspondence courses.