A Novel Architecture of SQU SMART LMS: The New Horizon for SMART City in Oman

Al-Kindi, Iman; Al-Khanjari, Zuhoor

doi:10.1109/icssit48917.2020.9214141

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…There is a relationship between these factors. Information on each student's engagement, behaviors, personality, and performance help teachers alter their teaching methods and take any required precautions to improve learning settings [26]. The availability of a tool that can clearly comprehend and regulate student's EBPersP.…”

Section: Discussionmentioning

confidence: 99%

“…These could include things like interest, time on task studies that look at effort quality and willingness to participate in learning activities [23]; ii) Student behavior: Students and the learning environment interact in two ways through behaviors, with the goal of causing desired changes in what students know and can do [24]; iii) Student personality: Individual variances in cognition, emotion, and behavior patterns are referred to as personality [25]; iv) Student performance: Normally, student performance is based on students' grades in any course. The idea is to help instructors predict their students' performance and analyze data in a more intelligent way in the classroom [26].…”

Section: Set Measurement Prioritiesmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Set Measurement Prioritiesmentioning

confidence: 99%

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

Al-Kindi

Al-Khanjari²,

Jamoussi³

2022

IJERE

Self Cite

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“…As a result, the proposed SQU-SLMS framework could make the interaction between students and instructors more smartly in any selected course using Moodle LMS [25]. This also offers instructors added value to help them to answer student needs based on their preferred learning materials.…”

Section: Discussionmentioning

confidence: 99%

Proposing A Systematic Framework For SQU-Smart Learning Management System (SQU-SLMS)

Al-Khanjari¹,

Al-Kindi²

2021

IJCDS

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Using a systematic framework could enhance the educational atmosphere to be creative. It is very important to use smart technology, which integrates innovative technology features to enhance the learning process. This could be taken care of by adopting an effective tool to create a smart educational environment and an effective personalized learning system. To create a smart educational environment, it requires smart tools to help instructors to carefully prepare the learning process, creative content of learning and appealing lessons. Also, smart tools are needed to demonstrate the results of learning in an attractive manner. This paper aims to propose a systematic framework called "SQU-SLMS" for developing a smart learning environment. It uses an existing LMS (i.e. Moodle), which is used at Sultan Qaboos University (SQU). Also, it uses a smart technical-predictive model, which is an incorporated component into a Smart Learning Management System (SLMS). The proposed technical framework consists of several components layers: users, user interface management authentication and authorization, smart aspects and database. The proposed SQU-SLMS has been compared with existing frameworks and ISC/IEC 12207:2008 and ISO/IEC 19796-1 standards. Also, the proposed systematic framework has been evaluated using ISO/IEC 9126-1. One of the outstanding outcomes that can be of a great value to instructors, is that the proposed SQU-SLMS framework can be used as an indicator that supports their teaching and learning process to keep monitoring student performance in a smart way and with less effort.

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“…However, they only focused on four factors: Student's Engagement, Behavior, Personality and Performance. The main aim of the current research is to help instructors forecast the performance of their students before the end of the semester and then evaluate the data in a smart learning environment [29]. The performance factor is represented by the total mark of the (Assignment 1, Mid Exam, Report, Weekly Discussions, Assignment 2 and Final Exam), the full mark of all assessments course is out of 100.…”

Section: Course Informationmentioning

confidence: 99%

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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A Novel Architecture of SQU SMART LMS: The New Horizon for SMART City in Oman

Cited by 7 publications

References 17 publications

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

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

Proposing A Systematic Framework For SQU-Smart Learning Management System (SQU-SLMS)

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

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