Evaluation of the SA-E System for Analysis of Students’ Real-Time Feedback

Altrabsheh, Nabeela; Cocea, Mihaela; Fallahkhair, Sanaz; Dhou, Khaldoon

doi:10.1109/icalt.2017.57

Cited by 16 publications

(16 citation statements)

References 1 publication

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“…Although, there have been some research studies that attempted to solve this problem by using opinion mining [2], [25], [26], [29] but their work is confined to just finding the polarity of an overall sentence (i.e. positive or negative).…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

et al. 2019

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Students' feedback is crucial for academic institutions in order to evaluate faculty performance. Handling the qualitative opinions of students efficiently while automatic report generation is a challenging task. Indeed, most organizations deal with quantitative feedback effectively, whereas qualitative feedback is either processed manually or ignored altogether. This study proposes a supervised aspect based opinion mining system based on two-layered LSTM model. The first layer predicts the aspects described within the feedback and later specifies the orientation (positive, negative, and neutral) of those predicted aspects. The model was tested on a manually tagged data set constructed from the last five years students' comments from Sukkur IBA University as well as on a standard SemEval-2014 data set. Unlike many other LSTM models proposed for other domains, the proposed model is quite simple in terms of architecture which results in less complexity. The system attains a good accuracy using the domain embedding layer in both tasks: aspect extraction (91%) and sentiment polarity detection (93%). To the best of our knowledge, this study is a first attempt that uses deep learning approach for performing aspect based sentiment analysis on students' feedback for evaluating faculty teaching performance.

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Section: Introductionmentioning

confidence: 99%

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

et al. 2019

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“…The authors introduce a system that can be easily used to assess answers to both numerical and algebraic type word problems and automatically identifies the exact errors made by students by using a marking rubric. (Wijesinghe, Kadupitiya, Ranathunga, & Dias, 2017) Question Others Evaluation This paper presents a system capable of automatically assessing student answers consisting of Venn and Euler diagrams (Altrabsheh et al, 2017) Essay Others…”

Section: Discussionmentioning

confidence: 99%

“…The first one focuses on sending feedback directly to the students, like a dashboard or recommendation, for example, based on an automatic method to analyze their behavior or performance (Lewkow et al, 2016). In this context, the range of applications goes from help students in a question-answering application (Alinaghi & Bahreininejad, 2011;Zhu, 2015) to support students in collaborative environments (Dascalu et al, 2015) and send feedback in real-time (Altrabsheh, Cocea, Fallahkhair, & Dhou, 2017). The second approach uses ETM to extract information to aid instructors in the elaboration of feedback from different resources (Goldin, Narciss, Foltz, & Bauer, 2017).…”

Section: Student Feedbackmentioning

confidence: 99%

Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

148

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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“…34 Altrabsheh et al presented that students feedback their opinions through Twitter during the whole lecture, used the SA-E system to analyze real-time feedback and displayed the visualized results. 35 However, this approach causes students to focus too much on the SA-E system, and ignoring the lecture content or familiarity with the system will also affect the feedback result. There are few studies on student facial sentiment recognition.…”

Section: Students' Real-time Feedback Methodsmentioning

confidence: 99%

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

Tian

Tang

Zhu

et al. 2022

Concurrency and Computation

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Sentiment analysis has a wide application prospect in business, medicine, security and other fields, which provides a new perspective for the development of education. Students' sentiment data play an important role in the evaluation of teachers' teaching quality and students' learning effect, and provide a basis for the implementation of effective learning intervention. However, most of the research is to obtain the real-time learning status of students in the classroom through teachers' naked eye observation and students' text feedback, which will lead to some problems such as incomplete feedback content and delayed feedback analysis. Based on the mini-Xception framework, this article implements the real-time identification and analysis of student sentiment in classroom teaching, and the degree of student engagement is analyzed according to the teaching events triggered by teacher to provide reasonable suggestions for subsequent teaching progress. The experimental results show that the mini-Xception model trained by FER2013 data sets has high recognition accuracy for the real-time detection of seven student sentiments, and the average accuracy is 76.71%. Compared with text feedback, it can assist teachers in understanding student learning states in time so that they can take corresponding actions, and realize the real-time performance of wisdom classroom teaching information feedback, the high efficiency of information transmission, and the intelligence of information processing.

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Evaluation of the SA-E System for Analysis of Students’ Real-Time Feedback

Cited by 16 publications

References 1 publication

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation

Text mining in education

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

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