Administrative Leadership Behaviours of Principals of Colleges of Education and their Impact on Tutors' Job Performance in the Volta Region, Ghana

Pajibo, Edison; Dzikunu, Cosmos Kwame; Asare, Mercy Obesebea

doi:10.20448/804.4.1.70.83

Cited by 1 publication

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“…It has also been defined as the ability to regulate emotions at the individual, group and crossboundary levels (Peterson, 2015). It is considered by some researchers (Mupfumira & Nyaruwata, 2021;Nasir & Masrur, 2010;Pajibo, Dzikunu, & Asare, 2019;Piczon & Asis, 2019;Turi, Ghani, Javid, & Sorooshian, 2020) to be a predictor of academic performance and a prerequisite for academic achievement. Payton et al (2008) describe social and emotional learning as a process by which all individuals (adults and children alike) acquire the skills necessary to, among other things, recognize and manage their emotions, establish and maintain positive relationships, and effectively manage interpersonal situations.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Modelling Students’ Socio-Emotional Attributes to Predict Their Performance in Intelligent Tutoring Systems

Assielou

Haba

Kadjo

et al. 2021

JEELR

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Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called SoEmo-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.

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