Research for Personalized Learning Reasource Recommendation Model based on Academic Emotions

Shi, Kaicheng; Wen, Bangchun; Wang, Jun; Ouyang, Zhaoxiang

doi:10.1109/csei47661.2019.8938863

Cited by 4 publications

(4 citation statements)

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“…This aligns with prior studies, a rming the potential of cognitive behavioral group counseling in bolstering psychological resilience in urban primary school students [20]. The results also attest to the notion that psychological resilience can be honed through systematic training [21]. The e ciency and positive outcomes of cognitive behavioral group counseling can be attributed to its interactive approach.…”

Section: Discussionsupporting

confidence: 88%

Study on the influence and intervention of psychological resilience on academic emotion of urban primary school students: based on cognitive-behavioral group counseling

Li,

Chen,

Sun

2023

Preprint

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This study delves deeply into the interrelationship between urban primary school students' psychological resilience and their academic emotions, shedding light on the transformative impact of cognitive behavioral group counseling on enhancing psychological resilience. With a sample size of 575 students across five schools in Huangshan City, Anhui Province, we distributed meticulously designed questionnaires. Our findings elucidate that a robust psychological resilience not only augments positive academic emotions (t = 0.374, 0.591, P < 0.01) but also diminishes the prevalence of negative ones (t= -0.541, -0.664, P < 0.01).Taking our research a notch higher, we enlisted 36 students and bifurcated them into two distinct groups: experimental and control. While the former underwent a structured regimen of cognitive behavioral group counseling designed to boost psychological resilience, the latter participated in after-school sports activities. Pre-intervention assessments showcased negligible disparities in both psychological resilience and academic emotions between the groups (P > 0.05). However, post-intervention revealed a notable enhancement in the psychological resilience of the experimental group (t = 2.546, P < 0.05). This group also demonstrated a significant upswing in positive academic emotions compared to the control group (t = 2.546, 3.607, P < 0.01) and a marked reduction in negative academic emotions (t=-3.761, -2.038, P < 0.05).The symbiotic relationship between psychological resilience and academic emotions in urban primary school students becomes evident through our study. Moreover, the prowess of cognitive behavioral group counseling as a potent tool for elevating psychological resilience and academic emotions is undeniably showcased. Such profound revelations carry monumental implications for educators and psychologists who are passionate about cultivating a student populace that is not only academically inclined but also emotionally balanced and resilient.

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Section: Discussionsupporting

confidence: 88%

Study on the influence and intervention of psychological resilience on academic emotion of urban primary school students: based on cognitive-behavioral group counseling

Li,

Chen,

Sun

2023

Preprint

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“…An optimized recommendation for personalized teaching and learning assessment tasks should consider academic emotion. Shi et al recommend learning materials based on learners' academic emotions to maintain positive academic emotions [5]. However, this paper only introduces academic emotion in the recommendation framework but does not analyze the relationship between learning performance and academic emotion.…”

Section: Related Workmentioning

confidence: 99%

“…To recommend the proper difficulty of an assessment task considering the effect of academic emotion, an Academic Emotion Influence (AEI) model is proposed in this paper, shown by (5).…”

Section: ) Recommending Proper Knowledge Items and Difficulty Conside...mentioning

confidence: 99%

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Agent-Based Personalized Assessment Tasks Recommendation Considering Objective and Subjective Factors

Cai

Niu

2023

IEEE Access

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In traditional education, there is not much difference between assessment tasks designed for learners. However, learners' learning performance may vary due to a number of factors, e.g., learning ability, academic emotion, and learners' and teachers' academic expectations. Considering those factors, accurately recommending personalized assessment tasks for each learner is challenging. To overcome the limitations in the current work, this paper proposed an autonomousagent-based approach to recommend personalized assessment tasks considering multiple factors. Contributions of the proposed approach contain three aspects: (1) Considering objective factors, the proposed approach dynamically adjusts the assessment tasks recommended for students. (2) Considering subjective factors, the proposed approach can dynamically predict learners' learning performances by applying autonomous agent-based negotiation. (3) The proposed recommendation algorithm can handle the problems of cold start in the research of typical recommendation algorithms. A set of experiments evaluates the effectiveness of the proposed approach in this paper, and the experimental results show that the proposed approach can accurately and adaptively generate personalized assessment tasks considering objective and subjective factors.

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State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning

et al. 2022

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Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization. This survey aims to examine the different works of literature on RSs that corroborate e-learning and classify and provide statistics of the reviewed articles based on their recommendation goals, recommendation techniques used, the target user, and the application platforms. The survey makes a prominent contribution to the e-learning RSs field by providing an overview of current research and traditional and nontraditional recommendation techniques to provide different recommendations for future e-learning. One of the most significant findings to emerge from this survey is that a substantial number of works followed either deep learning or context-aware recommendation techniques, which are considered more efficient than any traditional methods. Finally, we provided comprehensive observations from the quantitative assessment of publications, which can guide and support researchers in understanding the current development for potential future trends and the direction of deep learning-based RSs in e-learning.

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Research for Personalized Learning Reasource Recommendation Model based on Academic Emotions

Cited by 4 publications

References 0 publications

Study on the influence and intervention of psychological resilience on academic emotion of urban primary school students: based on cognitive-behavioral group counseling

Study on the influence and intervention of psychological resilience on academic emotion of urban primary school students: based on cognitive-behavioral group counseling

Agent-Based Personalized Assessment Tasks Recommendation Considering Objective and Subjective Factors

State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning

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