Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

Assielou, Kouamé Abel; Théodore, Cissé; Tra, Bi; Lambert, Tanon; Daniel, Kouakou

doi:10.14569/ijacsa.2020.0110728

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…Nevertheless, the Matrix Factorization technique has encountered several improvements such as: MRMF (Multi-Relational Matrix Factorization) [14], WMRMF (Weighted Multi-Relational Matrix Factorization) [15], So-WMRMF (Social Weigthed Multi-Relational Matrix Factorization) [16], Emo -WMRMF (Emotional Weigthed Multi-Relational Matrix Factorization) [15], SoEmo-WMRMF (Socio-Emotional Weigthed Multi-Relational Matrix Factorization) [17]. These approaches generally try to draw on several domain relationships.…”

Section: State Of the Artmentioning

confidence: 99%

Multi-relational Matrix Factorization Approach for Educational Items Clustering

Gono¹,

Goore²,

Tiecoura³

et al. 2023

IJIET

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Pedagogical models development requires several steps, one of which is the mapping of tasks and skills, also known as the educational items clustering. This activity of clustering educational items usually requires the participation of domain experts. However, discovering the exact skills involved in performing the tasks is a complex activity for them. This paper aims at solving the task and skill-mapping problem by proposing an approach based on the Weighted Multi-Relational Matrix Factoring technique to help experts in this task. This approach relies on two types of relationship, the “ student does task” relationship and the “student has skills” relationship through a latent factor model to reconstruct the “ task requires skill” relationship, the latter being the mapping between tasks and skills. An evaluation conducted on a group of two hundred (200) students in lower 6th class in a general secondary school (Côte d'Ivoire), showed that this approach brought an improvement rate of about 82.8% of the skill-task mapping proposed by the experts in the field. This result confirms that our approach not only allows us to map tasks and skills but also to significantly improve the updating of curricula.

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Section: State Of the Artmentioning

confidence: 99%

Multi-relational Matrix Factorization Approach for Educational Items Clustering

Gono¹,

Goore²,

Tiecoura³

et al. 2023

IJIET

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“…Some of the most important supervised (classification, regression), unsupervised (clustering), and reinforcement learning algorithms of machine learning are common as tools in biometrics or neuroscience research to detect emotions and affective attitudes, and are listed below: Among classification algorithms the most common choices are: naïve Bayes [ 158 , 159 , 160 ], Decision Tree [ 161 , 162 , 163 ], Random Forest [ 164 , 165 , 166 ], Support Vector Machines [ 167 , 168 , 169 ], and K Nearest Neighbors [ 170 , 171 , 172 ]. Among regression algorithms the usual choices are: linear regression [ 173 , 174 , 175 ], Lasso Regression [ 176 , 177 ], Logistic Regression [ 178 , 179 , 180 ], Multivariate Regression [ 181 , 182 ], and Multiple Regression Algorithm [ 183 , 184 ]. Among clustering algorithms the most common choices in biometrics or neuroscience research are: K-Means Clustering [ 185 , 186 , 187 ], Fuzzy C-means Algorithm [ 188 , 189 ], Expectation-Maximization (EM) Algorithm [ 190 ], and Hierarchical Clustering Algorithm [ 188 , 191 , 192 ].…”

Section: Brain and Biometric Affect Sensorsmentioning

confidence: 99%

“…Among regression algorithms the usual choices are: linear regression [ 173 , 174 , 175 ], Lasso Regression [ 176 , 177 ], Logistic Regression [ 178 , 179 , 180 ], Multivariate Regression [ 181 , 182 ], and Multiple Regression Algorithm [ 183 , 184 ].…”

Section: Brain and Biometric Affect Sensorsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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“…Isenman (2018) argues that emotion has above all a prospective or motivational role. For these two reasons, we recently proposed in Assielou, Haba, Gooré, Kadjo, and Yao (2020a), an FM approach we called So-WMRMF, which combines students' cognitive capacities and the power of group relations in a Multi-Relational framework. Unlike the work carried out by most of the authors of the related literature, in this approach, the characteristic vector of a student is influenced by the weighted average of the characteristic vectors of all the participants in the working group.…”

Section: Related Workmentioning

confidence: 99%

“…In the So-WMRMF approach proposed by Assielou et al (2020a), it is assumed that a student's s performance is affected by their s N work group friends in the following way: In Equation 5, 1 s w denotes the estimated characteristic vector of student s, given those of their work group companions.…”

Section: Weighted Multi-relational Matrix Factorization and Social (So-wmrmf) Approachmentioning

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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Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

Cited by 6 publications

References 19 publications

Multi-relational Matrix Factorization Approach for Educational Items Clustering

Multi-relational Matrix Factorization Approach for Educational Items Clustering

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

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

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