Understanding Students’ Online Reviews to Improve College Experience and Graduation Rates of STEM Programs at the Largest Post-Secondary Institution: A Learner-Centered Study

Muollo, Killian; Basavaraj, P; Garibay, Iván

doi:10.1109/fie.2018.8658450

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…Supervised and unsupervised [48], [49], [50], [51]. Supervised and lexicon-based [52], [53], [54], [55], [56], [57], [58], [59], [60], [61]. Unsupervised and lexicon-based [62], [63], [64].…”

Section: Learning Techniques Papersmentioning

confidence: 99%

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Oghu,

Ogbuju,

Abiodun

et al. 2023

Bull.Socinf.The.App

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The education industry considers quality to be a crucial factor in its development. Nevertheless, the quality of many institutions is far from perfect, as there is a high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, the literature lacks a reasonable number of systematic reviews that classify research that applied natural language processing and machine learning solutions for students’ sentiment analysis and quality assurance feedback. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in a high-impact journal-indexed database. The work extracted 59 relevant papers from the 3392 initially found using exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in education and the prevalent supervised machine learning algorithms, lexicon-based approaches, and evaluation metrics in assessing feedback in the education domain.

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Section: Learning Techniques Papersmentioning

confidence: 99%

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Oghu,

Ogbuju,

Abiodun

et al. 2023

Bull.Socinf.The.App

View full text Add to dashboard Cite

show abstract

“…In higher education, sentiment analysis has been used to analyse lecture and peer assessment in Massive Open Online Courses (MOOCs), and, how students react to online educational videos [see 39,40]. Sentiment analysis has also been used in evaluating students' perceptions on learning strategies, learning experiences and learning outcomes; as well as the learning experience gained from using a chatbot to support learning a language in an informal learning space [ See 41,[42][43][44].…”

Section: The Sentiment Analysis Processmentioning

confidence: 99%

Social Network and Sentiment Analysis: Investigation of Students’ Perspectives on Lecture Recording

2020

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This article presents the results of a study aimed at understanding the value of lecture recordings to student learning. We analysed transcripts of discussions on social media (Facebook) that students generated on the value of lecture recordings. Students discussed whether recording lectures and making them available should be compulsory. While the efficacy of lecture recording has been studied using conventional methods (e.g. questionnaires and interviews) on highly structured data, we employed social network and sentiment analysis techniques to examine individual messages posted on the Student Union's Facebook page. We chose to employ social network and sentiment analysis because these methods are useful in examining semi-structured and unstructured social media data. Overall findings suggest students generally view lecture recordings as resources for supplementing live lectures rather than replacing them. Students stated that lecture recordings could facilitate the creation of an inclusive learning environment and inculcate a positive learning experience. Work presented in this article adds to the growing debate on the institutional deployment of lecture recordings and their impact on students' engagement and learning. It also demonstrated how educational researchers could utilise social network and sentiment analysis to examine critical issues in education.

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“…Others, such as Chechi and Bhalla (2017), discussed a similar topic -factors influencing the students' university performance in mathematics. Furthermore, Muollo et al (2018) described the implementation of modern methods in education and conducted a students' survey to assess their impact on students' satisfaction. In his research, Yue (2011) analysed the potential to improve students' math skills in technology and engineering programs at the Newark University.…”

Section: Introductionmentioning

confidence: 99%

Non-Parametric Anova Methods Applied on Students' Performance Development in Course of Statistics

Zámková¹,

Prokop²,

Stolín³

2020

Acta Univ. Agric. Silvic. Mendelianae Brun.

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This study intends to use non-parametric statistical methods for comparison of mean values in assessing the changes of overall grades in basic statistics at the College of Polytechnics in Jihlava. The data covering students' grades were transferred to the ordinal scale and evaluated with the use of the corresponding non-parametric statistical methods, due to failing the data normality test. In order to identify statistically significant grade differences over the years 2006–2018, we have used the test of equality of means as well as the relevant analyses of variance, i.e. the Mann-Whitney U test to compare two samples, the Kruskal-Wallis test to compare multiple samples, and the post hoc analysis. All tests that were carried out confirm that on average, women and Finance and Management students tend to achieve better grades in statistics, as opposed to men, and Travel and Tourism students. Mean grades have rather fluctuated over the years. In the beginning (2006–2009) and in the end (2014–2017) of the observed period the grades have been significantly superior to the grades in the in-between stage (2010–2013). The identified fluctuation in grades shall lead to implementation of measures aimed at achieving an overall increase in statistics' education quality and students' grades.

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Understanding Students’ Online Reviews to Improve College Experience and Graduation Rates of STEM Programs at the Largest Post-Secondary Institution: A Learner-Centered Study

Cited by 5 publications

References 3 publications

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Social Network and Sentiment Analysis: Investigation of Students’ Perspectives on Lecture Recording

Non-Parametric Anova Methods Applied on Students' Performance Development in Course of Statistics

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