Individual differences matter in the effect of teaching presence on perceived learning: From the social cognitive perspective of self-regulated learning

Zhang, Yamei; Tian, Yuan; Yao, Liangshuang; Duan, Changying; Sun, Xin; Niu, Gengfeng

doi:10.1016/j.compedu.2021.104427

Cited by 25 publications

(31 citation statements)

References 35 publications

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“…One aspect pertains to 'what can be adapted to? '-various adaptive characteristics of the learner, such as cognitive traits (working memory capacity, inductive reasoning ability, and meta-cognitive skills) [1], interests, experiences, learning styles, context, and environment. Another aspect of the adaptation pertains to 'what can be adapted?…”

Section: Educational Learning Settingsmentioning

confidence: 99%

“…That is the 24 Gabor magnitude resides in the 1572864 (256 × 256 × 24) dimension, which would be too expensive. Therefore, the resulting Gabor magnitude is normalized to zero mean and unit variance presented in the form (1).…”

Section: Proposed Facial Emotion Recognition Techniquementioning

confidence: 99%

“…However, providing such personalized learning settings might likely go beyond the educational training resources and budgets of most institutions. The e-learnings setting [1] is one instance of a promising alternative to the traditional one-side-fit-all approaches and offers the advantage of being cost-effective [2]- [4]. The personalization of the educational instructions to a learner is achieved through the intelligent tutoring system (ITS) [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

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Recognizing facial emotions for educational learning settings

Kingsley

Inyang

Msugh

et al. 2022

IJRA

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Educational learning settings exploit cognitive factors as ultimate feedback to enhance personalization in teaching and learning. But besides cognition, the emotions of the learner which reflect the affective learning dimension also play an important role in the learning process. The emotions can be recognized by tracking explicit behaviors of the learner like facial or vocal expressions. Despite reasonable efforts to recognize emotions, the research community is currently constraints by two issues, namely : i) the lack of efficient feature descriptors to accurately represent and prospectively recogniz e (detecting) the emotions of the learner ; ii) lack of contextual datasets to benchmark performances of emotion recognizers in the learning - speci fic scenarios, resulting in poor generalizations. This paper presents a facial emotion recognition technique (FERT). The FERT is realized through results of preliminary analysis across various facial feature descriptors. Emotions are classified using the m ultiple kernel learning (MKL) method which reportedly possesses good merits. A contextually relevant simulated learning emotion ( SLE ) dataset is introduced to validate the FERT scheme. Recognition performance of the FERT scheme generalizes to 90.3% on the SLE dataset. On more popular but noncontextually datasets, the scheme achi e ved 90.0% and 82.8% respectively extended Cohn Kanade (CK+) and acted facial expressions in the wild ( AFEW ) datasets. A test for the null hypothesis that there is no significant difference in the performances accuracies of the descriptors rather proved otherwise ( x2 = 14 . 619 , df = 5 , p = 0 . 01212 ) for a model considered at a 95% confidence interval.

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Section: Educational Learning Settingsmentioning

confidence: 99%

Section: Proposed Facial Emotion Recognition Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recognizing facial emotions for educational learning settings

Kingsley

Inyang

Msugh

et al. 2022

IJRA

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“…This study aims to assess how much of a mediating factor teaching presence can be when confronted with self-regulated learning components of performance and self-reflection, both understood as indicators of perceived learning, which is also a valid indicator of learning outcomes ( Shea et al, 2019 ). These relationships become relevant considering that the relationship between teaching presence and learning outcomes and their interactions is a valuable research focus ( Zhang et al, 2022 ). There have been studies that have investigated the link between teaching presence and perceived learning, but findings have been inconsistent.…”

Section: Introductionmentioning

confidence: 99%

“…There have been studies that have investigated the link between teaching presence and perceived learning, but findings have been inconsistent. This may be due to the possibility that the relationship between teaching presence and perceived learning could be moderated by factors related to context, course types, and other individual factors ( Zhang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Teaching Presence vs. Student Perceived Preparedness for Testing in Higher Education Online English Courses During a Global Pandemic? Challenges, Tensions, and Opportunities

2022

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In the context of a global pandemic that started in 2020, the Chilean higher education institution Universidad Andrés Bello (UNAB) faced the challenge of giving continuity to its already established blended program for English courses while also starting the implementation of a high-stakes certification assessment for its students using the Test of English for International Communication (TOEIC) Bridge. This study sought to evaluate how much of a mediating factor online teaching presence could be in the context of test preparation within a language course in aspects related to autonomous learning and perceived learning outcomes. A mixed-methods approach was used. It included a survey applied to 1,642 eligible students of the English program. These quantitative data were complemented with students’ comments and teacher interviews. After triangulating quantitative and qualitative data, teaching presence was clearly perceived to be a relevant aspect of the online experience in the studied courses. However, both students’ and teachers’ voices evidenced pervasive challenges and tensions that hinder the potentially transformative benefits that online learning is expected to bring about.

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