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

Abstract

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.

Technology-mediated teaching and learning process: A conceptual study of educators’ response amidst the Covid-19 pandemic

“…The definition of emotional valence and its implication in respect to the different studied phenomenon or areas of its application, particularly within the educational domain, has been illustrated in the literature (Kort et al, 2001;Litman & Forbes-Riley, 2004;Okoye et al, 2020;Shen et al, 2009;Tian et al, 2010Tian et al, , 2018. As demonsttarted in this study, such type of analysis (allied to the text mining) is achieved by leveraging the underlying information (textual data) that are contained in the readily available datasets to draw useful insights or patterns about the population.…”

“…Technically, we used the get_nrc_sentiment function in R to extract the different (emotional valence) scores considering the responses by the students and their teachers. Typically, the get_nrc_sentiment functions by obtaining and quantifying (polarization) the intensities of the different terms (emotions) using the positive ( +), neutral (0) and negative (-) values (Litman & Forbes-Riley, 2004;Okoye et al, 2020) to represent each relevant term it finds in each case. In Fig.…”

“…Accordingly, as shown in Fig. 8, we adopted the emotion (sentiment) polarization or classifications of educational datasets as described in Litman and Forbes-Riley (2004) and Okoye et al (2020) to classify the different types of emotions we have observed for the stakeholders. This was done in order to determine the overall implication of the reported emotions for the teaching-learning processes amidst the Covid-19 pandemic.…”

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

“…The outcome of the exploratory study comprised of a survival model (Wen et al, 2014 ) that was built as a predictive/monitoring tool for determining the efficacy of certain human-expressions or language-behaviours (e.g., the impact of students' opinions in the MOOCs environment) based on the probability of certain events happening. 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 there is a need for innovative methods or approaches, such as the EPDM + ML model proposed in this paper, for extraction of educational-based information from the unprecedented datasets recorded and stored about the students' evaluation/recommendation of the teaching–learning performances, to help transliterate them into actionable plans for education in general. In our analysis, we extended the educational process and data mining (EPDM) model proposed in Okoye et al, ( 2020 ) to show how the amalgamation of the Text mining and Machine learning techniques which we grounded on the descriptive decision theory (Baucells & Katsikopoulos, 2011 ; Chandler, 2017 ), can be used to analyze the (educational) data (SET) towards improvement of the end-to-end teachers-students learning process and interactions within the higher educational setting. To this end, we proposed an educational process and data mining + machine learning (EPDM + ML) model for fostering the teaching analytics and performance evaluation.…”