RETRACTED: Efficient fuzzy based K-nearest neighbour technique for web services classification

Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

“…The fundamental feature of the Text mining (Altrabsheh et al, 2014 ; Binali et al, 2009 ; El-Halees, 2011 ; Pandey & Pandey, 2019 ; Wen et al, 2014 ) and Machine learning (Abu Alfeilat et al, 2019 ; Abu Zohair, 2019 ; Dey et al, 2016 ; Ghosh et al, 2020 ; Ofli et al, 2016 ; Viji et al, 2020 ) techniques, both in what can be defined in theoretical and technological paradigms; is that both methods can be used to understand the several patterns or relationships that exist in the (educational) datasets stored in the information systems or databases of the several organizations’ processes (Jones, 2019 ; Tur et al, 2017 ; van der Aalst, 2016 ). With Text mining, we note that the supported methods can be applied to determine the connections between the real-time processes and the intended users or stakeholders (Wen et al, 2014 ).…”

“…Also, taking into account the connectedness between the text mining technique (e.g., sentiment analysis) and machine learning or classification models (Ofli et al, 2016 ), the study of Dey et al ( 2016 ) notes that the sentiments which are often found in the comments or feedbacks (e.g., SET) can be categorized by polarity (i.e., positive, neutral, or negative Kalaivani, 2013 ; Litman & Forbes-Riley, 2004 ; Okoye et al, 2020 ), and then utilized to provide valuable pointers or indicators in connection to the various reasons or purposes for which the datasets are analyzed (e.g., the advances in teaching analytical methods and/or students’ evaluation of teaching described in this study). Besides, the authors (Dey et al, 2016 ) also used a statistical method that supports the K-nearest neighbour (KNN) (Abu Alfeilat et al, 2019 ; Ghosh et al, 2020 ; Viji et al, 2020 ) and Naïve Bayes’(Zhou et al, 2020 ) supervised machine learning algorithms to capture the different words/sentence polarities and elements of the subjective styles or patterns.…”

“…Along these lines, this study shows that through extraction of the different polarities or intensities of the sentiments and emotional valence expressed by the students in the SET data, that we are able to ascertain and provide new and vital information in relation to the top emotions the undergraduates (students) show when completing the SET instrument and/or rating of the teachers’ performance, and then provide strategies for further improvements through the machine learning and statistical analysis procedures. Consequently, we applied the k-nearest neighbour (KNN) classifier/algorithm (Abu Alfeilat et al, 2019 ; Ghosh et al, 2020 ; Viji et al, 2020 ) to model and make predictions about the students’ recommendation scores for the teachers based on the quantified (polarized) sentiments and emotional valence.…”

“…To note, Jones ( 2019 ) shows that Educators are consumably adopting the modern educational models to track, aggregate, and analyze the students learning behaviors or profiles that are logged in the databases of the several HEIs (Kori et al, 2018 ; Medne et al, 2020 ). Apparently, the most adopted technologies include the educational data mining (Bogarín et al, 2018 ; Romero & Ventura, 2013 , 2020 ), machine learning (Abu Alfeilat et al, 2019 ; Abu Zohair, 2019 ; Dey et al, 2016 ; Ghosh et al, 2020 ; Herodotou et al, 2019a , b ; Litman & Forbes-Riley, 2004 ; Muldner et al, 2011 ; Viji et al, 2020 ), and learning analytics (Ferguson, 2012 ; Ferguson & Clow, 2016 ; Larrabee Sønderlund et al, 2019 ; Mangaroska & Giannakos, 2019 ; Ndukwe & Daniel, 2020 ; Noroozi et al, 2019 ; Papamitsiou & Economides, 2014 , 2019 ). Interestingly, all of the aforenoted methods and studies have one common goal; which are directed towards achieving an effective and quality of Education, models, and learning outcomes (UNESCO, 2015 , 2021 ).…”

“…Whereas, the Text mining method is held to support the educational processes and/or information management, due to its capability to analyze and derive (new) relevant information from the textual datasets which are recorded in the various databases of the HEIs (Kumakawa, 2017 ; Lau et al, 2005 ; Tseng et al, 2018 ). The Machine learning classification model or technique is applied to make predictions based on the identified students' features or input datasets (Abu Zohair, 2019 ; Ghosh et al, 2020 ; Ofli et al, 2016 ; Viji et al, 2020 ; Wong & Yeh, 2019 ). Moreover, the study also believes that by efficiently using the extracted information or results of the two methods (Text mining and Machine learning) correctly, the higher educational institutions or educationalists can define adequate teaching analytical methods or innovative technologies that can not only be used to support the effectiveness of the teaching–learning processes in general, but can also be utilized to maintain a strong relationship amongst the stakeholders (e.g., HEIs, teachers, students) (Ndukwe & Daniel, 2020 ; Payne, 2006 ; Piedade & Santos, 2010 ; Renz & Hilbig, 2020 ; Tseng et al, 2018 ).…”