What can moment-by-moment learning curves tell about students’ self-regulated learning?

Abstract

Many students in primary education learn mathematics using adaptive learning technologies (ALTs) on tablets every day. Driven by developments in the emerging field of learning analytics, these technologies adjust problems based on learners' performance. Yet, until now it is largely unclear how students regulate their learning with ALTs. Hence, we explored how learners regulate their effort, accuracy and learning with an ALT using moment-by-moment learning curves. The results indicated that moment-by-moment learning curves did reflect students' accuracy and learning, but no associations with effort were found. Immediate drops were associated with high prior knowledge and suboptimal learning. Immediate peaks were associated with robust learning and pointed to effective student regulation. Close multiple spikes showed moderate learning and lower initial levels of accuracy but, with system support, these students seemed able to regulate their learning. Separated multiple spikes indicated reduced learning and accuracy and potentially signal the inability of students to regulate their learning. In this light, moment-by-moment learning curves seem to be valuable indicators of accuracy regulation during learning with ALTs and could potentially be used in interventions to support SRL with personalized visualizations.

The Effect of Emotions on Self-Regulated-Learning (SRL) and Story Comprehension in Emerging Readers

“…Additionally, this research can be used in classroom settings to analyze the impact of regulation and managing emotions. Using multimodal software, the effects of using SRL prompts and strategies can be analyzed in real time, so educators can understand the impact of managing emotions using SRL strategies (Bannert et al, 2015;Molenaar et al, 2019). This research can be used in the classroom to help educators understand the best ways to utilize SRL strategies in the moment to help students stay engaged and motivated during learning.…”

Young Learners’ Regulation of Practice Behavior in Adaptive Learning Technologies

“…In order to reach these goals, learners engage in cognitive activities (read, practice, elaborate) to learn a new topic. Metacognitive activities (orientation, planning, monitoring, and evaluation) help learners to control and monitor their learning to ensure effective and efficient learning (Veenman, 2013;Molenaar et al, 2019a). Affective states motivate learners to put in an appropriate level of effort to progress toward their learning goals (Azevedo et al, 2008).…”

“…Nevertheless an important element of regulation remains the task of the students, namely adjustment of effort to maintain accuracy (Winne, 2010;Hadwin, 2011). Accuracy is viewed as a function of knowledge and effort and learners can regulate accuracy by adjusting one or both of these elements (Molenaar et al, 2019a). ALTs often provide direct feedback indicating whether an answer is correct.…”

“…Although ALTs have been found to improve learning (Aleven et al, 2016a;Faber et al, 2017), the question of how learners regulate their learning using these technologies remains largely unanswered. Great diversity in learners' behavior during practice has been found with respect to the number of problems solved as well as the accuracy of problem-solving (Molenaar et al, 2019a). In addition to differences in prior knowledge, this variation could originate from differences in how learners regulate their learning (Arroyo et al, 2007;Paans et al, 2019).…”

Kualitas Sistem Pembelajaran Pengajar Tahsin dan Tahfizh di Organisasi Startup Pengajar Diponegoro Yogyakarta

“…Selain itu, guru berpotensi menggunakan alat diagnostik untuk menganalisis siswa mana yang membutuhkan dukungan sesuai definisi berikut: "ultimately to develop richer self-regulated learning skills. Also, teachers could potentially use a diagnostic tool to analyze which students are in need of support" (Molenaar, Horvers, & Baker, 2019).…”