Mining Opinions in User-Generated Contents to Improve Course Evaluation

El-Halees, Alaa M.

doi:10.1007/978-3-642-22191-0_9

Cited by 39 publications

(14 citation statements)

References 12 publications

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“…Understanding the emotional and cognitive aspects in the user reviews is essential for improving the scholarly understanding of an individual's learning experience in the online environment. Furthermore, exploring the learners' point of focus with regard to the course characteristics, such as the course content, teaching styles or platform construction (El‐Halees, 2011) and the user sentiment towards these focal points might be valuable for improving technological and pedagogical design.…”

Section: Introductionmentioning

confidence: 99%

Understanding the focal points and sentiment of learners in MOOC reviews: A machine learning and SC‐LIWC‐based approach

Geng¹,

Niu²,

Feng³

et al. 2020

Brit J Educational Tech

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Despite the popularity of massive open online courses (MOOCs), only a small portion of the course participants successfully complete the course. The low completion rate can be partially attributed to the mismatch between the participants' expectations and value delivered by the courses. Therefore, this study leverages MOOC reviews to investigate the focal point and sentiment of the learners by combining machine learning techniques and statistical analysis. Several text mining methods (ie, simplified Chinese‐linguistic inquiry and word count dictionary, word embeddings, and bidirectional long short‐term memory model) are combined to automatically extract the emotional and cognitive aspects, review focal point, and sentiment from the learner discourse. Multiple linear regression (MLR) analysis is performed to examine the relationships between the learner sentiment and the extracted content features. Using a set of real data from NetEase online open courses, our results reveal that the MOOC reviews mostly pertain to teaching and platform rather than the course content. Furthermore, the social process and personal concerns appear more frequently in the learner discourse. Overall, the learners exhibit positive attitudes towards teaching and platform and negative attitudes towards issues related to the course content. This study contributes to the literature regarding the MOOC research methodologies and provides a deeper understanding of the learner discourse behaviour in MOOCs.

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Section: Introductionmentioning

confidence: 99%

Understanding the focal points and sentiment of learners in MOOC reviews: A machine learning and SC‐LIWC‐based approach

Geng¹,

Niu²,

Feng³

et al. 2020

Brit J Educational Tech

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show abstract

“…Text mining methods, including sentiment analysis and opinion mining, are theoretical and technological accomplishments of the last decade that are closely related to natural language processing (NLP) (Pandey & Pandey, 2019), otherwise referred to as language technologies (Wen, Yang, & Rosé, 2014). The benefits of such methods and analyses can be understood through the different views or patterns that are contained in the databases of the organizations in question (Binali, Wu, & Potdar, 2009;El-Halees, 2011;Jones, 2019;Tur, Marín, & Carpenter, 2017). Interestingly, Wen et al (2014) note that text mining can be adopted to reveal relationships between the actual processes (the learning processes in social networks, government, businesses, for example) and the intended stakeholders.…”

Section: Conceptualizing Text Mining For Teaching and Learning Assessmentioning

confidence: 99%

Impact of students evaluation of teaching: a text analysis of the teachers qualities by gender

Okoye

Arrona‐Palacios

Camacho-Zúñiga

et al. 2020

Int J Educ Technol High Educ

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Today, modern educational models are concerned with the development of the teacher-student experience and the potential opportunities it presents. User-centric analyses are useful both in terms of the socio-technical perspective on data usage within the educational domain and the positive impact that data-driven methods have. Moreover, the use of information and communication technologies (ICT) in education and process innovation has emerged due to the strategic perspectives and the process monitoring that have shown to be missing within the traditional education curricula. This study shows that there is an unprecedented increase in the amount of text-based data in different activities within the educational processes, which can be leveraged to provide useful strategic intelligence and improvement insights. Educators can apply the resultant methods and technologies, process innovations, and contextual-based information for ample support and monitoring of the teaching-learning processes and decision making. To this effect, this paper proposes an Educational Process and Data Mining (EPDM) model that leverages the perspectives or opinions of the students to provide useful information that can be used to enhance the end-to-end processes within the educational domain. Theoretically, this study applies the model to determine how the students evaluate their teachers by considering the gender of the teachers. We analyzed the underlying patterns and determined the emotional valence of the students based on their comments in the Students Evaluation of Teaching (SET). Thus, this work implements the proposed EPDM model using SET comments captured in a setting of higher education.

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“…In [8] a model of sentiment analysis is presented to extract the opinions of the students to evaluate the quality of a course in two steps. In the classification of opinion, machine learning methods have been applied to classify an opinion as positive or negative for the publications of each student.…”

Section: Sentiment Analysis In Learning Environmentsmentioning

confidence: 99%

Emotion Recognition for Education using Sentiment Analysis

Barrón-Estrada¹,

Zataraín-Cabada²,

Bustillos³

2019

RCS

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Most systems that recognize emotions that are used in learning systems are oriented towards the detection of basic emotions such as happy, sad or angry. However, an intelligent tutor system must be able to detect, in a student, secondary emotions that have to do with learning as boring or frustrated. In the case of sentiment analysis in text, most of the research focuses on recognizing polarities (positive and negative). In this paper we present a sentiment analyzer for the recognition of polarities and emotions centered on learning using textual phrases written in Spanish in the field of computer science that can be used in an ITS (or any learning environment) to detect sentiments and/or emotions of the students. We also present the results obtained from the sentiment analyzer, which are very promising with 88.26% of accuracy.

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Mining Opinions in User-Generated Contents to Improve Course Evaluation

Cited by 39 publications

References 12 publications

Understanding the focal points and sentiment of learners in MOOC reviews: A machine learning and SC‐LIWC‐based approach

Understanding the focal points and sentiment of learners in MOOC reviews: A machine learning and SC‐LIWC‐based approach

Impact of students evaluation of teaching: a text analysis of the teachers qualities by gender

Emotion Recognition for Education using Sentiment Analysis

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