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Factors affecting student learning performance: A causal model in higher blended education.

Authors :
Ramirez‐Arellano, Aldo
Acosta‐Gonzaga, Elizabeth
Bory‐Reyes, Juan
Hernández‐Simón, Luis Manuel
Source :
Journal of Computer Assisted Learning. Dec2018, Vol. 34 Issue 6, p807-815. 9p. 3 Diagrams, 3 Charts.
Publication Year :
2018

Abstract

In Mexico, approximately 504,000 students pursue a bachelor's degree by means of distance or blended programmes. However, only 42% of these students conclude their degree on time. In the context of blended learning, the focus of this research is to present a causal model, based on a theoretical framework, which describes the relationships concerning motivations, emotions, cognitive strategies, metacognitive strategies, and learning strategies, and their impact on learning performance. The results suggest that negative emotions play a meaningful role between expectancy (a component of motivation) and learning strategies. Also, the expectancy component of motivation positively influences metacognitive strategies. Concerning the relationship between cognition and metacognition, metacognitive strategies take preference concerning the relationship between metacognitive and learning strategies, supporting the theoretical hypothesis that metacognitive processes are on a higher plane than cognition, and affect cognitive process directly. Moreover, the learning outcomes are directly influenced by cognitive and learning strategies, but not by metacognitive ones. Similarly, motivation has direct effects on metacognitive and learning strategies but not on cognitive ones. Lay Description: What is currently known about the subject matter: Several studies describe separately the causal relationships among motivation, emotions, cognition, metacognition, and learning performance in various learning contexts (mainly in distance and face‐to‐face learning).Although the fuzzy boundaries of metacognition have been recognized and a well‐sounded definition has been established, the theoretical relations with motivation, emotions, and cognition have barely been confirmed. What this paper adds to this: From the systems theory point of view, motivation, emotions, cognition, and metacognition are emergent subsystems of the human mind. Thus, studying each of them separately cuts off the relationships that conform the whole system and gives us a limited view of it.This integrating idea is depicted in a causal model, based on a theoretical framework, which describes the relationships among motivations, emotions, cognitive strategies, metacognitive strategies, and learning strategies, and their impact on learning performance.This research offers practical evidence that supports the theoretical relation between motivation and metacognition. The implications of study findings for practitioners. The motivation (expectancy) increases the use of metacognitive and learning strategies; this finding has practical implications, because metacognition may be positively stimulated by knowing that an individual's efforts to learn (control of learning beliefs) and judgments about the individual's abilities to accomplish a given task (self‐efficacy)Moreover, the results suggest that metacognitive, cognitive, and learning strategies are tightly related, in a hierarchical structure where metacognition plays an important role.The causal relations from positive emotions to metacognitive, cognitive, and learning strategies were not significant. Thus, the impact of negative emotions on the reviewed learning content (which captures the computer‐assisted nature of blended learning) and overall grade was explored. Students that face several obstacles in reviewing the learning content have not enough control to overcome this situation, and frustration is instigated. Also, anxious students think in advance that they will may fail the course, and this thought negatively impacts on their overall grade. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02664909
Volume :
34
Issue :
6
Database :
Academic Search Index
Journal :
Journal of Computer Assisted Learning
Publication Type :
Academic Journal
Accession number :
132914546
Full Text :
https://doi.org/10.1111/jcal.12289