Directing Teacher Focus in Computer Science Online Learning Environments

Lê, Katrina; Tarmazdi, Hamid; Vivian, Rebecca; Falkner, Katrina; Szabo, Claudia; Falkner, Nickolas

doi:10.1109/latice.2018.00014

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…Table (6): Methods used in Sentiment Analysis in Education Sector Learning Techniques Papers Unsupervised Machine Learning [3], [4], [7], [11], [14], [19], [20], [24], [25], [29], [32], [35], [39], [42], [43], [46], [48], [49], [53], [54], [56], [57], [60], [62], [66], [75], [78], Supervised and unsupervised Machine Learning [9], [10], [23], [27], [28], [31], [34], [36], [37], [41], [44], [63], [64], [65], [69], [70] Supervised Machine Learning [1], [2], [5], [6], [8], [12],…”

Section: Resultsmentioning

confidence: 99%

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Oghu,

Ogbuju,

Abiodun

et al. 2023

AIMS Research Publication

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The idea of modernization has become a norm in society, and the quality of the education sector that is been delivered to society is worth being assessed, and this will reflect the caliber of graduands these institutions are producing. As the key to this assessment is to x-ray clear-cut metrics used in the assessment of these institutions of higher learning by regulating authorities, parents, and other stakeholders These institutions' academic deliverables in terms of availability of experienced teachers, modern teaching and learning amenities, open access to learning materials, open access to assessment results, and timely responses to transcripts application to mention just a few needs proper evaluation. A lot of institutions' indices of performance are not encouraging, which translates to a high level of students dropping out, poor student performance, and low productivity which affects the standard of living and overall planned change that education is supposed to bring to the generality of the society at large. We must appreciate the efforts of previous researchers who have spent precious time using various sentiment analysis tools to unveil these challenges facing our education institutions and providing far-reaching measures that will make these institutions improve all indexes of measurement approved by regulating authorities globally as a mark of global competitiveness. Literature reviews by researchers using natural language processing and other machine learning techniques to get students and other stakeholders’ feedback by applying sentiment analysis to improve the quality of teaching and learning in the education sector have yielded the required results. Given the above this research effort aimed at reviewing published papers geared towards improving learners' experiences and enhancing quality assurance in the education system from published articles between 2016 to 2023 in referenced databases from high-impact journals. The research study collated 80 useful articles from the pool of 4595 that were earlier surfed using eligibility criteria which entailed employing exclusion and inclusion characteristics. The outcome of the literature reviews showed that 70% of researchers have used sentiment analysis as feedback and improvement analysis tools in enhancing the student learning experience and quality assurance in the higher education sector. The highly used sentiment analysis algorithms are lexicon-based, Support Vector Machine (SVM), Naïve Bayes(NB), Linear Regression(LR), and Artificial Neural Network(ANN). Keywords: Sentiment Analysis, Education Sector, Supervised Machine Learning Algorithms, social media, Questionnaires, Lexicon-based, Natural Language Processing(NLP) Emughedi Oghu, Emeka Ogbuju, Taiwo Abiodun & Francisca Oladipo.. (2023): Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning. Journal of Advances in Mathematical & Computational Science. Vol. 10, No. 4. Pp 1-20 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V11N3P1

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Section: Resultsmentioning

confidence: 99%

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Oghu,

Ogbuju,

Abiodun

et al. 2023

AIMS Research Publication

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“…Finally, SA was specifically used in two studies to improve teaching methods and feedback. Hence, Le et al (2018) developed a module to notify teachers when a student was experiencing difficulties, as they argued that negative, distressed, and questioning sentiments can determine when a student is experiencing those difficulties. Complementary, SA of teachers' productions (e.g., videos or pdf documents) developed by Cobos et al ( 2019) could help them make informed decisions whether or not it would be useful to modify their educational materials.…”

Section: Enhancing Teaching Methods and Feedbackmentioning

confidence: 99%

“…Forum messages and emails become the main data sources on learning assessment in online Higher Education (Alencar & Netto, 2020;Camacho & Goel, 2018;Chaabi et al, 2019;Le et al, 2018;Osorio Angel et al, 2020). In blended scenarios, other data sources as video transcriptions and readings have been used (Cobos et al, 2019).…”

Section: Information Extractionmentioning

confidence: 99%

“…Hence, although only four studies present frontend solution, many other studies analyzed in this research acknowledge that the next steps in SA should go towards the development of more easy-to-use visual analytics tools for teachers and students, both at individual and at a social and collaborative levels, that would help instructors providing effective feedback interventions (Dehbozorgi et al, 2020;Le et al, 2018). This development would benefit from integrating usability tests for teachers to maximize SA tools for personalizing the information displayed by the user, such as dynamic filters (by students' group, by each student, by academic year…), dynamic grouping (so teachers can group their own charts and add notes) and dynamic data labels (so teachers can more easily read the data).…”

Section: Rq2 Frontend Software Based On Sa Developedmentioning

confidence: 99%

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Sentiment analysis for formative assessment in higher education: a systematic literature review

Grimalt‐Álvaro

Usart

2023

J Comput High Educ

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Sentiment Analysis (SA), a technique based on applying artificial intelligence to analyze textual data in natural language, can help to characterize interactions between students and teachers and improve learning through timely, personalized feedback, but its use in education is still scarce. This systematic literature review explores how SA has been applied for learning assessment in online and hybrid learning contexts in higher education. Findings from this review show that there is a growing field of research on SA, although most of the papers are written from a technical perspective and published in journals related to digital technologies. Even though there are solutions involving different SA techniques that can help predicting learning performance, enhancing feedback and giving teachers visual tools, its educational applications and usability are still limited. The analysis evidence that the inclusion of variables that can affect participants’ different sentiment expression, such as gender or cultural context, remains understudied and should need to be considered in future developments.

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Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review

2022

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Directing Teacher Focus in Computer Science Online Learning Environments

Cited by 3 publications

References 23 publications

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Sentiment analysis for formative assessment in higher education: a systematic literature review

Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review

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