Listening to the voice of students, developing a service quality measuring and evaluating framework for a special course

Tóth, Zsuzsanna; Surman, Vivien

doi:10.1108/ijqss-02-2019-0025

Cited by 14 publications

(15 citation statements)

References 43 publications

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“…Recently, the report between Feb-June (TEC, 2020a) shows that the institution have invested over 82,483 h in the monitoring of the teaching of the model, with over 135,828 online sessions facilitated by the teachers covering over 3,480,568 students during this period, which in the same vein, may explain the learning progression of the students as observed in the results of the data analysis in this study. More importantly this shows the implication of the innovative models and students' choices under the current circumstances and formative experiences for the educators in terms of future learning management and strategies (Nagel et al, 2020;Tóth & Surman, 2019). Besides, we note from the MFD Experience and Emotion Survey analysis that only 18% of the students required extra support with their learning and well-being during this period.…”

Section: Hyflex + Tec Hybrid Learning Mode (Post-covid)mentioning

confidence: 75%

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

et al. 2021

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The COVID-19 pandemic has disrupted many areas of the human and organizational ventures worldwide. This includes new innovative technologies and strategies being developed by educators to foster the rapid learning-recovery and reinstatement of the stakeholders (e.g., teachers and students). Indeed, the main challenge for educators has been on what appropriate steps should be taken to prevent learning loss for the students; ranging from how to provide efficient learning tools/curriculum that ensures continuity of learning, to provision of methods that incorporate coping mechanisms and acceleration of education in general. For several higher educational institutions (HEIs), technology-mediated education has become an integral part of the modern teaching/learning instruction amidst the Covid-19 pandemic, when digital technologies have consequently become an inevitable and indispensable part of learning. To this effect, this study defines a hybrid educational model (HyFlex + Tec) used to enable virtual and in-person education in the HEIs. Practically, the study utilized data usage report from Massive Open Online Courses (MOOCs) and Emotions and Experience Survey questionnaire in a higher education setting for its experiments. To this end, we applied an Exponential Linear trend model and Forecasting method to determine overall progress and statistics for the learners during the Covid-19 pandemic, and subsequently performed a Text Mining and Univariate Analysis of Variance (ANOVA) to determine effects and significant differences that the teaching–learning experiences for the teachers and students have on their energy (learning motivation) levels. From the results, we note that the hybrid learning model supports continuity of education/learning for teachers and students during the Covid-19 pandemic. The study also discusses its innovative importance for future monitoring (tracking) of learning experiences and emotional well-being for the stakeholders in leu (aftermath) of the Covid-19 pandemic.

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Section: Hyflex + Tec Hybrid Learning Mode (Post-covid)mentioning

confidence: 75%

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

et al. 2021

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“…On the other hand, Bitner and Hubbert (1994) point out that satisfaction could also be considered at the individual transaction or encounter level. In alignment with that, there are also models which focus on the lower levels of institutional operation which characterize these service encounter level interactions (Noaman et al , 2017; Yousapronpaiboon, 2014; Sadeh and Garkaz, 2015; Kincsesné et al , 2015; Tóth and Surman, 2019). On the transaction level students are the participants who directly receive and use the services offered by the lecturers which makes them priority customers of educational services (Marzo-Navarro et al , 2005; Sander et al , 2000; Hill, 1995).…”

Section: Literature Reviewmentioning

confidence: 99%

What constitutes quality to students in higher education? An empirical investigation of course-level student expectations

Tóth

Bedzsula

2021

QAE

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Purpose The purpose of this paper is to identify and interpret the critical quality attributes of core educational services at the course level both with student and lecturer involvement. Significant differences between the viewpoints of these two directly involved stakeholder groups are also demonstrated to provide a better understanding of student expectations. Methodology Students of quality management courses were invited to collect those attributes that could have an impact on their perceived educational service quality. The compiled list of 23 characteristics has been formed on the basis of a four-point Likert scale-based questionnaire. With approximately 360 responses, thorough statistical analyses have been executed to investigate whether any significant differences could be detected between the quality attributes perceived by the different student segments. A group of lecturers has also been invited to fulfil the same questionnaire to compare their viewpoints with those of the students. Findings The results allow us to identify critical quality attributes which may be used in all platforms and interactions with students. The conclusions can be implemented on the course level to adjust the plan-do-check-act-based improvement of courses in which lecturers are directly interested. Originality As the new generation of students increasingly regard themselves as customers, they have become more aware of how they are taught and how they participate in the learning process. Institutionalizing this approach may contribute to the shaping of the organizational quality culture by emphasizing student focus and may result in the identification of best practices and standardization of processes at the course level.

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“…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 ). Therefore, in theory, the resultant model (EPDM + ML) and methodological approach used in this study shows to be important towards the promotion and advancement of the stakeholders´ experiences and/or teaching–learning performances/outcomes at large (Bowdre, 2020 ; De Fortuny et al, 2013 ; Dollinger & Lodge, 2018 ; Er et al, 2019 ; Gomes & Ma, 2020 ; Hilliger et al, 2020 ; Pedró et al, 2019 ; Sánchez et al, 2019 ; Tóth & Surman, 2019 ; Yadav & Berges, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching–learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching–learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers’ performances and recommendations based on data derived from students’ evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student’s recommendation for the teachers may be based on information about the students’ gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students’ recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed ( n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students’ comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments ( p = .000). While the males appear to be slightly borderline in terms of emotions ( p = .056) and sentiment ( p = .077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students’ recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1...

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Listening to the voice of students, developing a service quality measuring and evaluating framework for a special course

Cited by 14 publications

References 43 publications

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

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

What constitutes quality to students in higher education? An empirical investigation of course-level student expectations

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

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