Tracing elementary school students’ study tactic use in gStudy by examining a strategic and self-regulated learning

How do students of different self-efficacy regulate learning in collaborative design activities? An epistemic network analysis approach

Introduction

Students' self-regulation skills and self-efficacy are linked to performance and are considered essential for lifelong learning. Understanding these skills and their development is crucial for educational success and long-term personal growth.

Methods

In this study, 60 students attending a university-level collaborative design course were recruited as participants. They were initially classified into three groups [high, mixed, and low self-efficacy (SE)] based on the initial test results. Students' written reflections were then analyzed using epistemic network analysis (ENA), aiming to explore the characteristics and developmental trajectories of self-regulated learning (SRL).

Results

Comparing with the other two groups, the high self-efficacy (HSE) group demonstrated: (1) more behavioral characteristics of SRL in the performance and self-reflection stages, (2) an earlier development of interest 91 in the task and recognition of its value during collaborative design activities, 92 followed by the utilization of more cognitive and metacognitive strategies; and (3) an "anticipation-behavior-reflection" loop in the self-regulation process.

Discussion

These findings highlight the importance of fostering high self-efficacy among students to enhance their self-regulated learning capabilities and overall academic performance. Strategies for improving learners' SRL and future research directions were provided accordingly.

Investigating learning processes through analysis of navigation behavior using log files

The empirical study investigates what log files and process mining can contribute to promoting successful learning. We want to show how monitoring and evaluation of learning processes can be implemented in the educational life by analyzing log files and navigation behavior. Thus, we questioned to what extent log file analyses and process mining can predict learning outcomes. This work aims to provide support for learners and instructors regarding efficient learning with computer-based learning environments (CBLEs). We evaluated log file and questionnaire data from students (N = 58) who used a CBLE for two weeks. Results show a significant learning increase after studying with the CBLE with a very high effect size (p < .001, g = 1.71). A cluster analysis revealed two groups with significantly different learning outcomes accompanied by different navigation patterns. The time spent on learning-relevant pages and the interactivity with a CBLE are meaningful indicators for Recall and Transfer performance. Our results show that navigation behaviors indicate both beneficial and detrimental learning processes. Moreover, we could demonstrate that navigation behaviors impact the learning outcome. We present an easy-to-use approach for learners as well as instructors to promote successful learning by tracking the duration spent in a CBLE and the interactivity.

Note-taking and science inquiry in an open-ended learning environment

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Usage of digital notepad is related to performance in science inquiry tasks in OELE.

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Both taking and reaccessing notes facilitate science inquiry performance.

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Elaborative and reproductive notes’ relationship with success is content dependent.

Note-taking is important for academic success and has been thoroughly studied in traditional classroom contexts. Recent advancements of technology have led to more students taking notes on computers, and in different situations than are common in traditional instructional contexts. However, research on computer-based note-taking is still an emerging area, and findings from these studies are mixed. In this exploratory study, we conducted multilevel analysis to comprehensively investigate the relationship between note-taking measures and subsequent student success at science inquiry among middle school students, using two scenarios of an open-ended learning environment named Virtual Performance Assessments. Analysis revealed an advantage for content elaborative note-taking over content reproductive note-taking conditional on the source of notes taken, but other measures were less consistent between the two scenarios. Implications of the findings and limitations of this research are also discussed.

Exploring Socially Shared Regulation in the Context of Collaboration

Socially shared regulation of learning refers to processes by which group members regulate their collective activity. Successful individuals regulate their motivational, cognitive, and metacognitive engagement. Our hypothesis is that successful groups also share in regulating group processes. Following our earlier conceptual and empirical work on the social aspect of motivating and regulating learning (Hadwin & Järvelä, 2011; Järvenoja & Järvelä, 2009; Järvelä, Volet, & Järvenoja, 2010), our research questions are as follows: (a) What challenges do individuals and groups report experiencing during collaborative group work? (b) How do students collectively regulate these challenges at the time, and in future collaborations? (c) How do collaborative learning outcomes compare between groups with varying degrees of emerging shared regulation? We present an empirical study in which 18 graduate students worked in collaborative teams of 3–4 over an 8-week period. The nStudy (Winne, Hadwin, & Beaudoin, 2010) software was used for collaborative planning and work, as well as face-to-face and online collaboration between team members. Data included individual and collaborative statements about collaborative challenges, collaborative statements about contextual and future regulation strategies, collaborative learning performance, and log file traces of students’ contributions to collaborative chat discussions and planning activities. Findings indicated that the students expressed multiple challenges resulting in 3 kinds of regulation over time profiles: strong, progressive, and weak shared regulation. We also conclude that successful collaboration not only requires self-regulation but also allows each team member to support fellow team members to successfully regulate their learning and the team to come together to collectively regulate learning.

Triggers of Students’ Efficacious Interaction in Collaborative Learning Situations

This article presents a case study that investigated primary school students’ social interaction while working in small groups in science. The aim was to identify what characterizes and triggers students’ efficacious interaction in collaborative learning situations. This was done by exploring and analyzing students’ and groups’ task involvement and the quality of their activity. The micro-level analysis proceeded first by identifying episodes of group task involvement and evaluating the quality of these interaction episodes. Next, the transitions between episodes were explored by identifying what triggered the shifts between task involvement levels. Ten hours of video observation data captured fourth-grade students, aged 9 to 10, in two groups of 3 students studying “the vital conditions of life” for 5 weeks. The main findings indicated that efficacious interaction demanded collaboration between group members and required active participation and productive on-task working. Three types of triggers were found to cause changes in students’ activity, of which the group progress trigger was the most powerful for increasing, and the contextual trigger the most powerful for decreasing the activity. These findings shed light on certain aspects of efficacious interaction that will help us to identify details of efficacious interaction in future studies.