A Learning Performance Assessment Model Using Neural Network Classification Methods of e-Learning Activity Log Data

Arumugam, S.; Kovalan, A.; Narayanan, Arun

doi:10.1109/icssit46314.2019.8987958

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…The updated numeric data file is taken, and classification techniques are applied to predict the students' results. The RBFNN-based classification is better for predicting the grades of the students among the implemented models J48, random tree, multilayer perceptron neural network (MLPNN), radial basis neural network (RBFNN) than other classification techniques as proved by Arumugam et al [13].…”

Section: Introductionmentioning

confidence: 96%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

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The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/ learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student's fraternity as it is during this COVID-19 pandemic period.

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Section: Introductionmentioning

confidence: 96%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

View full text Add to dashboard Cite

show abstract

“…The following screen-shot explains that simple GUI form to run the algorithms repeatedly during the evaluation [3]. As shown in the following graphs, the confidence of getting high or low marks in a session is very much coping with the Cyclomatic Complexity of Sessions calculated in the previous study [5].…”

Section: The Implementation Results and Discussionmentioning

confidence: 97%

“…In our previous works [2], [3], the converted multidimensional text data are taken into training and testing the grades available in the excel file. Then it was analyzed using a J48 classifier and random tree classifier, neural network algorithms like MLPNN and RBFNN.…”

Section: Dataset and Algorithms Used In This Association Rule Minmentioning

confidence: 99%

“…In our previous work [1], instead of treating it as time-series data, we proposed a model to convert it as simple multidimensional numerical data to make it suitable to check with clustering and classification. In our previous works [1], [2], [3], we examined the dataset using J48, MLPNN, RBFNN algorithms and compared the precision, recall and f-measures.…”

Section: Introductionmentioning

confidence: 99%

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An Association Rule Mining Based Model to Predict Learning Performance of a student with e-Learning Activity Log Data

Arumugam¹,

Kovalan²,

Narayanan³

2020

IJITEE

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There is a wider scope in research on log data of computed aided learning and interactive learning. We have an enormous collection of log data of students’ activities during their learning process. Data Mining(DM) algorithms help us to discover knowledge and information from a huge and complex data sets. In a time-series log data, it is very complicated to verify the DM algorithms to mine the dataset. e-Learning activity log data is taken and converted into categorical data to predict the learning behavior of the students to implement the algorithms. The excavated knowledge can be used to modify the e-learning system. It is very easy from the result to note the slow learners and advanced learners well in advance before conducting an examination. Time series data is a numeric data that measures in a time period in successive order. The dataset used in this work is a UCI EPM dataset. It is a Non-Linear Time-Series Data. The converted dataset is used to apply a rule mining algorithm to predict the performance of a student. The measurements support and confidence will help us to predict the students’ performance. The results also have been compared with other classification mining algorithms. It assists to improve and to build an educational model on e-Learning. In turn it supports students, teachers, and educational system as well Learning Management System.

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Machine Learning in Higher Education: Students’ Performance Assessment Considering Online Activity Logs

et al. 2023

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A Learning Performance Assessment Model Using Neural Network Classification Methods of e-Learning Activity Log Data

Cited by 5 publications

References 16 publications

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

An Association Rule Mining Based Model to Predict Learning Performance of a student with e-Learning Activity Log Data

Machine Learning in Higher Education: Students’ Performance Assessment Considering Online Activity Logs

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