Artificial neural network analysis of the academic performance of students in virtual learning environments

Rivas, Alberto; González‐Briones, Alfonso; Hernández, Guillermo; Prieto, Javier; Chamoso, Pablo

doi:10.1016/j.neucom.2020.02.125

Cited by 68 publications

(59 citation statements)

References 24 publications

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“…The classification performance results are different when we use different algorithms, on different datasets, or depending on the number of different features of the same dataset. For example, in the research in [15], the highest accuracy with RF algorithm is 75.5%, which was performed on the dataset that the research built.…”

Section: Experiments On Smsm Resultsmentioning

confidence: 99%

“…It is shown that the data mining approach is more and more applied to education research. The popular algorithm could be found in research such as the K-mean clustering algorithm [23]; the classify algorithms are applied to build an understandable model, such as decision tree (DT), RF, extreme gradient boosting (EGB) in [15,21,24,25].…”

Section: Artificial Intelligence Methodologies In Educationmentioning

confidence: 99%

“…The teacher information and affectivity of them are shown in [8,9]. The course with content and features affectivity are shown in [8,9,15,16]. The infrastructure with component, structure, affectivity and ability are shown in [7,9,13,15,17,18].…”

Section: Affected Factorsmentioning

confidence: 99%

“…In the first phase, some commonly used classification algorithms are: DT, RF, EGB and MP in [15,21,24,25]. Therefore, we suggest five algorithms to experiment on such as RF, KNN, SVM and NB algorithm, with the aim of choosing the appropriate algorithm for the second phase.…”

Section: Algorithmsmentioning

confidence: 99%

“…With the classification model, some measurements are used in evaluating data classification result, by factors such as: accuracy, precision, recall, f1-score, ROC in [15,21] and our previous research in [24,25]. Thereby, we also suggest accuracy, precision, recall, f1-score, and ROC measurement to evaluate classifying model results.…”

Section: Evaluated Measurementmentioning

confidence: 99%

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The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

2020

Education Sciences

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The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.

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Section: Experiments On Smsm Resultsmentioning

confidence: 99%

Section: Artificial Intelligence Methodologies In Educationmentioning

confidence: 99%

Section: Affected Factorsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: Evaluated Measurementmentioning

confidence: 99%

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The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

2020

Education Sciences

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Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Gonzalez

Neyem

Contreras‐McKay

et al. 2022

Comp Applic In Engineering

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Students of Software Engineering Capstone Courses face situations and challenges that grant them valuable experiences. However, as this knowledge is acquired through real-life exposure, it is difficult to transmit it across different generations. To deal with this problem, it has been proposed that students record their learnings through a lesson learned model. But the question of how future students can best benefit from these records remains unanswered. This study addresses this problem through the use of Artificial Intelligence (AI) Virtual Assistant combined with a recommender system. Artificial Intelligence Virtual Assistants, as conversational chatbots, are a manifestation of AI via the simulation of conversation with human users. The technology has the potential to provide personalized service to a range of stakeholders. Thus, we seek to move this trend forward by now pushing these capabilities into the field of higher education. Consequently, we aim to assist software engineering students by leveraging collective knowledge to enhance learning experiences.

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Targeted projection pursuit similarity based attribute selection for academic performance prediction

Ramanathan

Balasubramanian

2022

Concurrency and Computation

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The performance of student in the academic field reveals the consideration over researchers to enhance student's weakness. With the consumption of high potential factors from the dataset, accurate student performance prediction is carried out. Targeted projection pursuit similarity based attribute selection (TPPS-AS) technique is designed to improve the student academic performance prediction. TPPS is a machine learning technique that observes the given input. The relevant attributes from the multidimensional space is determined by TPPS. Several research works were recognized recently to conclude the high potential factors for observing student academic performances. A novel technique is designed in this research work to improve the student academic performance prediction in a taken dataset by choosing more relevant attributes. To detect the student academic performance with better accuracy and lesser time, proposed TPPS-AS technique is employed. The performance of student academic performance prediction is improved by TPPS-AS technique through the attribute selection with higher accuracy. With this proposed technique, prediction accuracy of the student academic performance is increased after the relevant attributes selection process.

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Artificial neural network analysis of the academic performance of students in virtual learning environments

Cited by 68 publications

References 24 publications

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Targeted projection pursuit similarity based attribute selection for academic performance prediction

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