A Text Mining Examination of University Students’ Learning Program Posters

Kumakawa, Takehisa

doi:10.4236/oalib.1103639

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Perhaps, this could be owing to the fact that the unprecedented COVID-19 outbreak is recent and currently a hot topic being addressed both by the researchers, medical professionals, and the governments in the diaspora. Therefore, whereas the current review explores telemedicine within the context of LATAM during the pandemic, there may also be studies that could have addressed and applied the concept (telemedicine) for patient care and medical practice pre-Covid and/or Having said that, this study deemed it important to implement a text mining [53][54][55][56] analysis of the main findings and contribution of the analyzed (24 studies) papers. This was done in order to identify the most frequent and correlation (association) of the used words by the authors in describing the impact of telemedicine and its practice amidst the COVID-19 in LATAM.…”

Section: Authorsmentioning

confidence: 99%

Impact and future of telemedicine amidst the COVID-19 pandemic: a systematic review of the state-of-the-art in Latin America

Nieblas

Okoye

Carrión

et al. 2022

Ciênc. saúde coletiva

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Significant progress has been made in using information and communication technologies in medicine, by impacting the quality of health-care delivery system and patient care, and paving the way for ground-breaking tools for e-health and clinical decision-support systems. This study investigates the extent to which the evolution of telemedicine applications has been used to support patient care in Latin America (LATAM) amidst the pandemic. Theoretically, the study applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to identify the impact of telemedicine in the region. Practically, the paper provides a systematic mapping study of the different domain areas and methodological progress in Telemedicine that occurred during the pandemic, and applied a text mining technique to understand the intensities of the terms expressed by the analyzed studies. The results show that while telemedicine has not been extensively used, a greater percentage of the studies report that telemedicine was effective. Approximately 70% positive emotional valence score was found. The paper also provides an empirical discussion and recommendations for the next steps in ample adoption of telemedicine.

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Section: Authorsmentioning

confidence: 99%

Impact and future of telemedicine amidst the COVID-19 pandemic: a systematic review of the state-of-the-art in Latin America

Nieblas

Okoye

Carrión

et al. 2022

Ciênc. saúde coletiva

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“…Nowadays, text mining techniques are being applied within the educational sector, allowing researchers to analyze textual data such as formal documents, e-mails, chat messages, digital notes, and online discussions for teaching-learning improvement and decision-making purposes. For example, Kumakawa (2017) notes that to exploit the goldmine of educational data and its use to understand better how the students progress with learning, researchers should essentially employ data mining techniques. Figure 1 shows the Educational Process and Data Mining model (EPDM) proposed in this paper.…”

Section: Sentiment Analysis For Educational Process Innovation and Damentioning

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 this study, the text mining and machine learning technique was used to analyze the students’ evaluation of teaching and recommendation of the teachers based on the opinions or perceptions (e.g., sentiment analysis, and emotional valence) of the students, as well as determines what the students' recommendation may be based on the identified sentiments and emotional valence scores. 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 ).…”

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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A Text Mining Examination of University Students’ Learning Program Posters

Cited by 5 publications

References 8 publications

Impact and future of telemedicine amidst the COVID-19 pandemic: a systematic review of the state-of-the-art in Latin America

Impact and future of telemedicine amidst the COVID-19 pandemic: a systematic review of the state-of-the-art in Latin America

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

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

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