Measuring learning outcomes effectively in smart learning environments

Yassine, Sahar; Kadry, Seifedine; Sicilia, Miguel–Ángel

doi:10.1109/ssfc.2016.7447877

Cited by 21 publications

(15 citation statements)

References 8 publications

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“…From this log file, the authors focus on four factors: i) Student engagement: The term "student engagement" has been used to describe a variety of subjects. 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%

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: Set Measurement Prioritiesmentioning

confidence: 99%

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

Al-Kindi

Al-Khanjari²,

Jamoussi³

2022

IJERE

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“…The two-way communication between students and the learning environment are learning behavior. The purpose of these behaviors is to make the desired improvements in what students know and what they can do [8].…”

Section: Student's Behaviormentioning

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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“…The key areas and features taken into consideration while developing smart learning tools were discussed in [1,2,24]. One of the important aspects of a smart city is technology-enhanced learning and SLA.…”

Section: Literature Reviewmentioning

confidence: 99%

Mining Smart Learning Analytics Data Using Ensemble Classifiers

Kausar¹,

Oyelere²,

Salal³

et al. 2020

Int. J. Emerg. Technol. Learn.

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Recent progress in technology has altered the learning behaviors of students; besides giving a new impulse which reshapes the education itself. It can easily be said that the improvements in technologies empower students to learn more efficiently, effectively and contentedly. Smart Learning (SL) despite not being a new concept describing learning methods in the digital age- has caught attention of researchers. Smart Learning Analytics (SLA) provides students of all ages with research-proven frameworks, helping students to benefit from all kinds of resources and intelligent tools. It aims to stimulate students to have a deep comprehension of the context and leads to higher levels of achievements. The transformation of education to smart learning will be realized by reengineering the fundamental structures and operations of conventional educational systems. Accordingly, students can learn the proper information yet to support to learn real-world context, more and more factors are needed to be taken into account. Learning has shifted from web-based dumb materials to context-aware smart ubiquitous learning. In the study, a SLA dataset was explored and advanced ensemble techniques were applied for the classification task. Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively.

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Measuring learning outcomes effectively in smart learning environments

Cited by 21 publications

References 8 publications

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

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

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

Mining Smart Learning Analytics Data Using Ensemble Classifiers

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