Delali Kwasi Dake
3 Publications in AERD
Author Publications
Export PDFINTERNET OF THINGS (IOT) APPLICATIONS IN EDUCATION: BENEFITS AND IMPLEMENTATION CHALLENGES IN GHANAIAN TERTIARY INSTITUTIONS
By Delali Kwasi Dake
Aim/Purpose The Internet of Things (IoT) application modules have covered diverse sectors, and the educational domain is no exception. In this survey, we discuss the spe-cific application benefits of IoT in education and further examine implementa-tion challenges in Ghanaian tertiary institutions. Background This survey examines pertinent applications for IoT benefits in education and offers present and future opportunities to enhance educational outcomes. The survey includes anticipated IoT technologies that will have a significant impact on education. Each module contains concise definitions accompanied by analy-sis and application-specific relevance. Methodology In order to accomplish the objectives of the survey, a search review was con-ducted across relevant databases, including Scopus, Hindawi, IEEE, MPDI, Sci-enceDirect, Informing Science Institute, Springer, and Wiley. In addition, a thorough search was carried out using Google Scholar to cover all relevant re-positories. The phrases and keywords for the search were made up of five cate gories. The literature search resulted in 300 articles, of which 200 were consid-ered relevant for the survey. Of the 200 articles, 95 of them shared common themes and discussed the same application integration and challenges. Contribution This paper discusses the revolution involving IoT deployments in education and covers many aspects of the educational domain. Findings IoT integration in education will transform Education 4.0 and improve learning outcomes significantly. Recommendations for Practitioners Educational institutions are to embrace IoT integrations even with the emerging Education 4.0 and Industry 4.0 use cases. Recommendations for Researchers Educational IoT is the next big thing and research directions on unique use cases for educational institutions are eminent with 5G and other disruptive technologies. Impact on Society Effective IoT implementation in education will positively affect all stakeholders in the educational ecosystem and create a society with much access to infor-mation, connectivity, and convenience. Future Research To survey the integration of blockchain-based IoT applications in education. © This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes.
Using sentiment analysis to evaluate qualitative students’ responses
By Delali Kwasi Dake
Text analytics in education has evolved to form a critical component of the future SMART campus architecture. Sentiment analysis and qualitative feedback from students is now a crucial application domain of text analytics relevant to institutions. The implementation of sentiment analysis helps understand learners’ appreciation of lessons, which they prefer to express in long texts with little or no restriction. Such expressions depict the learner’s emotions and mood during class engagements. This research deployed four classifiers, including Naïve Bayes (NB), Support Vector Machine (SVM), J48 Decision Tree (DT), and Random Forest (RF), on a qualitative feedback text after a semester-based course session at the University of Education, Winneba. After enough training and testing using the k-fold cross-validation technique, the SVM classification algorithm performed with a superior accuracy of 63.79%. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
UNVEILING LEARNER EMOTIONS: SENTIMENT ANALYSIS OF MOODLE-BASED ONLINE ASSESSMENTS USING MACHINE LEARNING
By Delali Kwasi Dake
Aim/Purpose The study focused on learner sentiments and experiences after using the Moo-dle assessment module and trained a machine learning classifier for future senti-ment predictions. Background Learner assessment is one of the standard methods instructors use to measure students' performance and ascertain successful teaching objectives. In pedagogi-cal design, assessment planning is vital in lesson content planning to the extent that curriculum designers and instructors primarily think like assessors. Assess-ment aids students in redefining their understanding of a subject and serves as the basis for more profound research in that particular subject. Positive results from an evaluation also motivate learners and provide employment directions to the students. Assessment results guide not just the students but also the instruc-tor.Methodology A modified methodology was used for carrying out the study. The revised methodology is divided into two major parts: the text-processing phase and the classification model phase. The text-processing phase consists of stages includ-ing cleaning, tokenization, and stop words removal, while the classification model phase consists of dataset training using a sentiment analyser, a polarity classification model and a prediction validation model. The text-processing phase of the referenced methodology did not utilise tokenization and stop words. In addition, the classification model did not include a sentiment analyser.Contribution The reviewed literature reveals two major omissions: sentiment responses on using the Moodle for online assessment, particularly in developing countries with unstable internet connectivity, have not been investigated, and variations of the k-fold cross-validation technique in detecting overfitting and developing a reliable classifier have been largely neglected. In this study we built a Senti-ment Analyser for Learner Emotion Management using the Moodle for assess-ment with data collected from a Ghanaian tertiary institution and developed a classification model for future sentiment predictions by evaluating the 10-fold and the 5-fold techniques on prediction accuracy.Findings After training and testing, the RF algorithm emerged as the best classifier using the 5-fold cross-validation technique with an accuracy of 64.9%.Recommendations for PractitionersRecommendations for Researchers Instead of a closed-ended questionnaire for learner feedback assessment, the open-ended mechanism should be utilised since learners can freely express their emotions devoid of restrictions. Feature selection for sentiment analysis does not always improve the overall ac-curacy for the classification model. The traditional machine learning algorithms should always be compared to either the ensemble or the deep learning algo-rithmsImpact on Society Understanding learners' emotions without restriction is important in the educa-tional process. The pedagogical implementation of lessons and assessment should focus on machine learning integrationFuture Research To compare ensemble and deep learning algorithms
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