Using Data Mining Techniques on Moodle Data for Classification of Student’s Learning Styles

Kika, Alda; Leka, Loreta; Maxhelaku, Suela; Ktona, Ana

doi:10.20472/iac.2019.047.010

Cited by 8 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most of the cited works are oriented automatically discover student Learning Styles, and some of them use in different forms the Moodle platform mostly relying on logmining and alternative machine-learning methods (Kika et al 2019;Abdullah 2015; Surjono 2014; Karagiannis and Satratzemi 2018). These articles describe interesting approach, but do not provide any insight about the adequacy of Moodle to support the proposed extensions.…”

Section: Related Workmentioning

confidence: 99%

A software architecture perspective about Moodle flexibility for supporting empirical research of teaching theories

2020

View full text Add to dashboard Cite

Moodle represents a great contribution to the educational world since it provides an evolving platform for Virtual Learning Management Systems (VLMS) that became a standard de facto for most of the educational institutions around the world. Through the pedagogical functions provided, it collects in the many globally spread out databases a huge amount of information regarding the activities that teachers and students perform during the learning process. This reality makes Moodle a natural choice for conducting experimental research by Artificial Intelligence researchers interested in theories for improving learning and teaching; particularly those related with the controversial learning styles concept. Roughly defined, a learning style intends to be a model of the way and media an apprentice acquires knowledge and hence the way a teacher should present that knowledge to the apprentice matching his/her learning style. Independently of the many controversies (be these scientific, psychological or even ethical) about the soundness and real outcomes that such ideas can bring to improve learning, it's a worthy intriguing research area for many researchers pursuing the ideal automated teacher: the teachbot dream. Behind this goal we have developed Middle, a Moodle plug-in able to infer the learning style of each student taking a course using an advanced version of a Bayesian network model that we previously tested. Middle intends support personalized teaching based on the Felder-Silverman's ILS model and has been evaluated through controlled experiments and pilot test in high schools and university courses. Such experiments showed promising results that shed some light on learning styles modeling and its potential outcomes. During the experience we found strong limitations in the Moodle design regarding its supposed flexibility to incorporate new functionalities. From a strict software architecture point of view, we found that such flexibility is far from being enough to easier the implementation of the dynamic computational behavior required to support a teachbot. This made our effort much harder than expected, perhaps because of the illusion induced by the widespread use of Moodle. In this article we present our results and experiences extending Moddle with intelligent behavior from a software architecture point of view, focusing on the lessons learnt in such extension. Our experience indicates that this simplicity is far from being so and hence it is worth to share the limitations and how we overcome them.

show abstract

Section: Related Workmentioning

confidence: 99%

A software architecture perspective about Moodle flexibility for supporting empirical research of teaching theories

2020

View full text Add to dashboard Cite

show abstract

“…Research on learning style detection using an automatic approach mostly uses data-driven, which is the data log files. Besides, the study conducted aims to determine the best classification algorithm among Algorithm Decision Tree (J48), Artificial Neural Network, and Support Vector Machine to detect student's learning styles into eight learning styles FSLSM [5], [17], [18], [21], [23], [26], [44]. The results of the comparison of the performance of the classification algorithm to detect FSLSM learning styles provide the Naive Bayesian algorithm better than other Data Mining algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Testing the classification algorithm in this study in this conducted using a multi-class confusion matrix [46] n × n with n = 16, because it is used to analyze the classification of learning style detection containing 16 classes. If using a multiclass confusion matrix, the total number of false negatives (T F N ), false positives (T F P ), and true negative (T T N ) for each class number i will be calculated based on Generalized (24), (25), and (26). equations.…”

Section: Model Testingmentioning

confidence: 99%

“…Data-driven methods aim to build classification models that copy ILS instruments and use sample data to build models. Some classification techniques that are widely used to detect this learning style include Neural Network [17]- [19], Decision Tree [1], [6], [7], [20], [21], and Bayesian Network [22]- [26]. The literature-based method uses student behavior and actions with the system to identify their learning preferences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Hidayat¹,

Wardoyo²,

Sn³

et al. 2020

IJACSA

View full text Add to dashboard Cite

Learning Management System (LMS) is well designed and operated by an exceptional teaching team, but LMS does not consider the needs and characteristics of each student's learning style. The LMS has not yet provided a feature to detect student diversity, but LMS has a track record of student learning activities known as log files. This study proposes a detection model of student's learning styles by utilizing information on log file data consisting of four processes. The first process is pre-processing to get 29 features that are used as the input in the clustering process. The second process is clustering using a modified K-Means algorithm to get a label from each test data set before the classification process is carried out. The third process is detecting learning styles from each data set using the Naive Bayesian classification algorithm, and finally, the analysis of the performance of the proposed model. The test results using the validity value of the Davies-Bouldin Index (DBI) matrix indicate that the modified K-Means algorithm achieved 2.54 DBI, higher than that of original K-Means with 2.39 DBI. Besides having high validity, it also makes the algorithm more stable than the original K-Means algorithm because the labels of each dataset do not change. The improved performance of the clustering algorithm also increases the values of precision, recall, and accuracy of the automatic learning style detection model proposed in this study. The average precision value rises from 65.42% to 71.09%, the value of recall increases from 72.09% to 80.23%, and the value of accuracy increases from 67.06% to 71.60%.

show abstract

“…Moreover, building an adaptive learning management system based on learning styles is a very stimulating task. Two approaches to determine students culture style are mainly used: using questionnaires or data mining techniques on Learning Management Systems log data., (Kika, Leka, Maxhelaku, & Ktona, 2019)…”

Section: Introductionmentioning

confidence: 99%

Using Data Mining In Learning Management Systems Amidst Covid-19

Mobo

2020

View full text Add to dashboard Cite

The Second Semester of Academic Year 2019-2020 was temporarily suspended due to the widespread COVID-19 last March 16, 2020, forcing the President of the Republic of the Philippines, Hon. Rodrigo Roa Duterte imposed an Enhanced Community Quarantine in Luzon which is known as a lockdown closing all the border points of each town and provinces. One of the major problem encountered during the lockdown is the suspension of classes because as per IATF guidelines you need to stay home, the said Memorandum Order was posted in the official gazette, (Medialdea, 2020)The dataset on the features of the Learning Management Systems using Moodle is that Professors will be the one who will set the topics, quizzes, and exercises for his class even the assessment methods on the system. To prevent from slowing down the network, the Team of Seaversity the developer of the learning management systems headed by C/E Ephrem Dela Cernan conducts a ZOOM Training to all Faculty to be familiarized more on the Learning Management Systems of the Philippine Merchant Marine Academy. The Moodle Learning Management Systems is a user-friendly environment because of its features and users can easily adjust from the traditional face to face teaching going to e-Learning approach because of it’s all capabilities as a data mining methods such as statistics, association rule mining, pattern mining visualization, categorization, clustering, and text mining., (AlAjmi & Shakir, 2013)

show abstract

Using Data Mining Techniques on Moodle Data for Classification of Student’s Learning Styles

Cited by 8 publications

References 5 publications

A software architecture perspective about Moodle flexibility for supporting empirical research of teaching theories

A software architecture perspective about Moodle flexibility for supporting empirical research of teaching theories

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Using Data Mining In Learning Management Systems Amidst Covid-19

Contact Info

Product

Resources

About