Person-Oriented Approaches to Profiling Learners in Technology-Rich Learning Environments for Ecological Learner Modeling

Abstract

Technology-rich learning environments (TREs) provide opportunities for learners to engage in complex interactions involving a multitude of cognitive, metacognitive, and affective states. Understanding learners’ distinct learning progressions in TREs demand inquiry approaches that employ well-conceived theoretical accounts of these multiple facets. The present study investigated learners’ interactions with BioWorld, a TRE developed to guide students’ clinical reasoning through diagnoses of simulated patients. We applied person-oriented analytic methods to multimodal data including verbal protocols, questionnaires, and computer logs from 78 task solutions. [...

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

“…Future research should further examine the directionality of this relationship, and whether SRL strategies can act as a buffer to the relationship between emotions and learning outcomes during literacy tasks. The results of this study are in line with findings indicating that negative emotions are associated with unfavorable learning outcomes (D'Mello and and the use of less favorable learning strategies (Jang et al, 2017;Price et al, 2018). This finding is also in line with previous research which has found that negative emotions are related to decreases in scores on reading activities (Grills-Taquechel et al, 2012;Daley et al, 2014).…”

“…This result suggests that during a narrative storytelling task, planning and cognitive control could employ similar processes, as planning might scaffold the goals and expectations of the task enough to cue the students to engage in thought modification strategies involved in cognitive control. The general finding that SRL strategies are related to increases in storytelling scores is supported by previous research with older adolescents and undergraduate students, which has demonstrated that using SRL strategies is related to increases in reasoning, and deeper learning (D'Mello and Zheng, 2016;Jang et al, 2017). Although it was expected that monitoring would be related to learning outcomes, it is possible that engaging in planning strategies, such as clarifying the objectives and sub goals of the task, is more important to success on this type of task, since comprehension can be revised and reconstructed based on novel story events.…”

“…These findings significantly contribute to the current literature on emotions, SRL strategies and literacy outcomes, as few studies have examined the relationship between these three variables. While some studies have looked at the relationship between SRL strategies and learning outcomes (D'Mello and Zheng, 2016;Jang et al, 2017), few studies have examined the relationship between emotions and SRL strategies (Price et al, 2018;Taub et al, 2019), and of these studies, fewer have established connections to learning outcomes. This study looked at broad macro-level SRL strategies, such as planning, monitoring and cognitive control (Bannert et al, 2015) instead of investigating the micro-level behaviors (e.g., memory retrieval statements, checking understanding) within these broad strategies (Greene and Azevedo, 2009).…”

“…While there has been extensive research on SRL and learning, very little research has been conducted on the relationship between emotions and SRL strategies, and their joint effect on learning outcomes. Previous research has found that learning gains are related to the use of advanced SRL strategies (Zheng, 2016;Jang et al, 2017), which help learners manage and regulate emotions to maintain focus on the task (Calvo and D'Mello, 2011;D'Mello and Graesser, 2012). Additionally, previous research has demonstrated that students with strong emotion regulation skills both experience less intense negative emotions, and use more metacognitive strategies during learning (Price et al, 2018).…”

Mobile learning for professional development: Significant factors, attitudes, behaviours and engagement patterns

“…Previous literature on learner profiling has generally adopted two major approaches: personoriented (also referred to as person-centred), and technology-based. The former often involved cluster analysis or latent analysis and was adopted by a number of researchers in profiling learner motivational profiles (Chon & Shin, 2019;Corpus & Wormington, 2014;Hayenga & Corpus, 2010;Jang et al, 2017;Ratelle, Guay, Vallerand, Larose, & Senécal, 2007). On the other hand, the technology-based approach to learner profiling, which involved collecting learners' information generated automatically using computerised algorithms and stored digitally, was also widely adopted by many (Becking et al, 2004;Brusilovsky, 1996;Graf, 2015;Nehiri & Aknin, 2017;Snyman & van den Berg, 2018).…”