Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique

Ahmad, Nor Bahiah; Shamsuddin, Siti Mariyam; Abraham, Ajith

doi:10.1007/978-3-642-11224-9_5

Cited by 6 publications

(3 citation statements)

References 27 publications

(44 reference statements)

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“…Significant characteristics have been established related to the Felder Silverman learning dimension: active/reflective, sensor/intuitive, visual/verbal and sequential/global. They noticed that the preferences of students were consistent with the characteristics of the learning styles described in the Felder Silverman model [18]. Abdullah suggested an approach, based on their learning style, to dynamically distinguish students.…”

Section: Student's Behaviormentioning

confidence: 83%

Exploring Factors and Indicators for Measuring Students’ Performance in Moodle Learning Environment

Al-Kindi

Al-Khanjari

2021

Int. J. Emerg. Technol. Learn.

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One of the most important pillars of smart cities is the smart learning environ-ment. This environment should be well prepared and managed to improve the in-struction process for instructors from one side and the learning process for stu-dents from the other side. This paper presents the student’s Engagement, Behav-ior and Personality (EBP) predictive model. This model uses Moodle log data to investigate the influence and the effect of the students’ EBP factors on their per-formance. For this purpose, this paper uses the data log files of the "Search Strat-egies on the Internet" online course in Fall 2019 at Sultan Qaboos University (SQU) extracted from Moodle database. The intention of conducting this kind of experiments is of three-facets: 1. to assist in gaining a holistic understanding of online learning environments by focusing on student EBP and performance with-in the course activities, 2. to explore whether the student’s EBP can be considered as indicators for predicting student’s performance in online courses, and 3. to support instructors with insights to develop better learning strategies and tailor instructions for personal learning of individual students. Moreover, this paper takes a step forward in identifying effective methods to measure student’s EBP during the learning process. This may contribute to proposing a framework for the smart learning behavior environment that would guide the instructors to ob-serve students’ performance in a more creative way. All the 38 students who participated in this experiment had compatible statistics and results as the relationship between their Engagement, Behavior, Personality was symmetric with their Performance. This relationship was presented using a group of condition rules (If-then). The extracted rules gave us a straightforward and visual picture of the rela-tionship between the factors mentioned in this paper.

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Section: Student's Behaviormentioning

confidence: 83%

Exploring Factors and Indicators for Measuring Students’ Performance in Moodle Learning Environment

Al-Kindi

Al-Khanjari

2021

Int. J. Emerg. Technol. Learn.

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“…• Applicability and compatibility to the principles of interactive learning system designs [34] • Suitability with a hypermedia learning system that incorporate varying types of multimedia-based learning materials [35] • Practicality and convenience for establishing students' dominant preferences in a mobile learning environment [36] • Appropriateness and feasibility for language learning [37] [38] The information about learners' learning styles will be gathered through mobile-based questionnaires designed to accurately identify the preferred learning styles within mobile environment. The questionnaires used to provide the profile of learners' learning preferences are adapted from the Index of Learning Style (ILS) questionnaire developed by Felder and Soloman [39] based on the Felder-Silverman Learning Style Model (FSLSM).…”

Section: B Tailoring I-mol To Learning Stylesmentioning

confidence: 99%

Designing an Intelligent Mobile Learning Tool for Grammar Learning (i-MoL)

Shuib¹,

Abdullah²,

Azizan

et al. 2015

Int. J. Interact. Mob. Technol.

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English is the most important second language in most non-English speaking countries, including Malaysia. A good English proficiency comes from good grasp of grammar. To conquer the problems of low English proficiency among Malaysians, it is important to identify the key motivators that could facilitate the process of grammar learning. In this digital age, technology can play a very important role and mobile technology could be one of it. Thus, this study aims at designing a mobile learning tool, namely the Intelligent Mobile Learning Tool for Grammar Learning (i-MoL) to act as the “on-the-go” grammar learning support via mobile phones. i-MoL helps reinforce grammar learning through mobile phone with game-like applications, inquiry-based activities and flashcard-like information. The intelligent part of i-MoL lies in its ability to map the mobile-based grammar learning content to individual’s preferred learning styles based on Felder-Silverman Learning Style Model (FSLSM). The instructional system design through the ADDIE model was used in this study as a systematic approach in designing a novel and comprehensive mobile learning tool for grammar learning. In terms of implications, this study provides insights on how mobile technologies can be utilized to meet the mobility demand among language learners today.

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“…In addition, when using the Moodle system, researchers looked at student preferences. [16] advocated using the Integrated Felder Silverman learning style model into the establishment of student qualities. Students' preferences matched the Felder Silverman model's descriptions of learning styles, they discovered.…”

Section: Introductionmentioning

confidence: 99%

Extracting student patterns from log file Moodle course: A case study

Al-Kindi

Al-Khanjari²,

Jamoussi³

2022

IJERE

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<span lang="EN-US">This paper introduces a set of extracted factors from Moodle log file of the selected course as a case study that aims to capture student Engagement (E), Behavior (B), Personality (Pers) and Performance (P). The factors are applied to identify students’ EBPersP with different course activities. The data set used in this paper was selected from the "Introduction to Computer Science" online course that captures 273,906 records as a log file for 29 students, delivered in Spring 2020. The paper also tries to show whether there is a relationship between student engagement, behavior and personality and their performance. Results show different patterns of students’ interactions with course contents, activities, and assessments. Specifically, our findings highlight that students' EBPersP could be extracted from Moodle log files. In addition, the extracted factors could assist instructors on how to focus more on students with low and average performance, giving them more attention to enhancing their performance. </span>

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Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique

Cited by 6 publications

References 27 publications

Exploring Factors and Indicators for Measuring Students’ Performance in Moodle Learning Environment

Exploring Factors and Indicators for Measuring Students’ Performance in Moodle Learning Environment

Designing an Intelligent Mobile Learning Tool for Grammar Learning (i-MoL)

Extracting student patterns from log file Moodle course: A case study

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