Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

Villegas-Ch, William; Román-Cañizares, Milton; Palacios-Pacheco, Xavier

doi:10.3390/app10155371

Cited by 110 publications

(68 citation statements)

References 45 publications

(61 reference statements)

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“…Current educational situations necessitate developing and implementing alternative strategies to replace the previous functions of learning evaluation settings due to COVID-19. The integration of artificially intelligent components into educational systems greatly helps to efficiently provide meaningful information for evaluation and learning [39][40][41]. On the one hand, adaptive testing technology offers an efficient and reliable personalized evaluation system.…”

Section: Discussionmentioning

confidence: 99%

Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks

Choi

McClenen

2020

Applied Sciences

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Online formative assessments in e-learning systems are increasingly of interest in the field of education. While substantial research into the model and item design aspects of formative assessment has been conducted, few software systems embodied with a psychometric model have been proposed to allow us to adaptively implement formative assessments. This study aimed to develop an adaptive formative assessment system, called computerized formative adaptive testing (CAFT) by using artificial intelligence methods based on computerized adaptive testing (CAT) and Bayesian networks as learning analytics. CAFT can adaptively administer personalized formative assessment to a learner by dynamically selecting appropriate items and tests aligned with the learner’s ability. Forty items in an item bank were evaluated by 410 learners, moreover, 1000 learners were recruited for a simulation study and 120 learners were enrolled to evaluate the efficiency, validity, and reliability of CAFT in an application study. The results showed that, through CAFT, learners can adaptively take item s and tests in order to receive personalized diagnostic feedback about their learning progression. Consequently, this study highlights that a learning management system which integrates CAT as an artificially intelligent component is an efficient educational evaluation tool for a remote personalized learning service.

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Section: Discussionmentioning

confidence: 99%

Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks

Choi

McClenen

2020

Applied Sciences

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“…This tool allows performing analytical queries in core SQL language that facilitates obtaining results and allows presenting knowledge through graphical structures to improve decision-making. The results are evaluated again to determine that the results that will be presented to the end user meet the conditions established in the initial question [39]. If these do not present clarity in the answer, the process returns to the data warehouse created by Hive, adds the necessary data, and repeats the processing until the results are satisfactory.…”

Section: How the Data Analysis Model Workmentioning

confidence: 99%

Academic Activities Recommendation System for Sustainable Education in the Age of COVID-19

2021

Self Cite

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Currently, universities are going through a critical moment due to the coronavirus disease in 2019. To prevent its spread, countries have declared quarantines and isolation in all sectors of society. This has caused many problems in the learning of students, since, when moving from a face-to-face educational model to a remote model, several academic factors such as psychological, financial, and methodological have been overlooked. To exactly identify the variables and causes that affect learning, in this work a data analysis model using a Hadoop framework is proposed. By processing the data, it is possible to identify and classify students to determine the problems they present in different learning activities. The results are used by an artificial intelligence system that takes student information and converts it into knowledge, evaluates the academic performance problems they present, and determines what type of activity aligns with the students. The artificial intelligence system processes the information and recommends activities that focus on each student’s abilities and needs. The integration of these systems to universities creates an adaptive educational model that responds to the new challenges of society.

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“…Distance Education Learning Environment Survey is also one of the commonly used parameters for deciding the sub-scales in each of the factors affecting student satisfaction (Walker and Fraser, 2005). Other than regression and other statistical techniques machine learning has also been used for the improvement of online education model (Villegas-Ch, Román-Cañizares and Palacios-Pacheco, 2020). The authors proposed the integration of machine learning with learning management systems in order to improve student satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Student Satisfaction in the Current Online Teaching Scenario

Nair¹,

Nair²

2021

JEET

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The current COVID situation has put the entire education system in a state of shock and has forced the educators to adopt online teaching at a rapid pace. Faculty and students are working hard to adapt to new and continuously evolving methods of and to make it as close to a classroom experience as possible. Hence an analysis of student satisfaction for a specific course is planned through analytical means in this article. Data is collected using the distance education learning environments survey as it closely resembles the current online teaching scenario. The survey captures the relationship between student satisfaction and parameters such as faculty support, student interaction, active learning, student autonomy, authentic learning and personal relevance. The survey is designed as a five point Likert type set of choices for each of the parameters. The participants of this study were 150 undergraduate students of Second year of Kalasalingam Academy of Research and Education who were taking a common course in Mathematics. From the survey results, correlation analysis and descriptive statistics is conducted to understand the parameters which considerably affect the student satisfaction considerably. The analysis shows that faculty interaction and student interaction were the most significant factors affecting student satisfaction. These results can directly act as an input to the institutions which are finding the aspect of online classes challenging and will motivate them to address the key issues directly to improve their student satisfaction.

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Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

Cited by 110 publications

References 45 publications

Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks

Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks

Academic Activities Recommendation System for Sustainable Education in the Age of COVID-19

Analysis of Student Satisfaction in the Current Online Teaching Scenario

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