How do Gender, Learning Goals, and Forum Participation Predict Persistence in a Computer Science MOOC?

Crues, R. Wes; Henricks, Genevieve M.; Perry, Michelle; Bhat, Suma; Anderson, Carolyn J.; Shaik, Najmuddin; Angrave, Lawrence

doi:10.1145/3152892

Cited by 36 publications

(34 citation statements)

References 30 publications

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“…For example, the experiments conducted by Wen et al (2014) showed that the forums and online discussion platforms for students in the massive open online courses (MOOCs) could provide ample opportunity to engage learners, even in social processes (Brinton et al, 2014;Crues et al, 2018). For this purpose, they proposed a survival modeling approach to analyze the impact of students' opinions over time in MOOCs.…”

Section: Conceptualizing Text Mining For Teaching and Learning Assessmentioning

confidence: 99%

“…This work shows that the EPDM model can help provide innovative practices within the HEI setting that range from the adoption of intelligent methodologies to the transformation of students' learning experiences by enabling the so-called three-dimensional expressive-communication-relational skills. The series of experiments and analyses presented in this paper is useful for improvement of the teaching practices that students value, primarily because it addresses gender stereotypes and teacher-student engagement (Crues et al, 2018;Sánchez et al, 2019). The model we present is a Competency-Based Education (CBE) model (UNESCO, 2015;Yadav & Berges, 2019), and it is adequate and effective in supporting the new educational process initiatives and practices (TEC, 2018).…”

Section: Conceptualizing Text Mining For Teaching and Learning Assessmentioning

confidence: 99%

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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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Section: Conceptualizing Text Mining For Teaching and Learning Assessmentioning

confidence: 99%

Section: Conceptualizing Text Mining For Teaching and Learning Assessmentioning

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

View full text Add to dashboard Cite

show abstract

“…Massive Open Online Courses (MOOCs) are widely regarded as a new revolution of education, and has become a hot cross research subject of education, psychology, information technology, and data science [1] . The joint efforts of academia and industry have led to the recent development of multiple MOOC platforms, such as Coursera, Udacity, Edx, and XuetangX, to adequately address diverse learning needs and cater to service learners by providing thousands of well-designed online courses.…”

Section: Introductionmentioning

confidence: 99%

“…Even though many studies have reported good dropout prediction accuracy, two shortcomings have been observed in the related literature to date. (1) The objective of dropout prediction is to predict whether a learner exhibits a learning behavior during several consecutive days in the future. Therefore, the information associated with the learning behaviors of learners on several consecutive days should be considered.…”

Section: Introductionmentioning

confidence: 99%

Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs

Wen

Tian

Wen

et al. 2020

Tinshhua Sci. Technol.

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Recently, Massive Open Online Courses (MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning, this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network (CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.

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“…Whitehill et al [11], in contrast, analyzed persistence at course level in an online context and they considered that students were persistent when they interacted with the course at least once a week. Similarly, Crues et al [12] defined three levels of persistence (low, medium and high) depending on how many weeks students had worked in the course. Regardless of the scope of this definition of persistence (at the course or degree level), this definition of persistence aims to capture to what extent a student keeps on doing an activity in a long period (e.g., course/degree).…”

Section: Introductionmentioning

confidence: 99%

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

et al. 2020

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In education, several studies have tried to track student persistence (i.e., students’ ability to keep on working on the assigned tasks) using different definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students’ interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students’ profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly affected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students’ average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.

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How do Gender, Learning Goals, and Forum Participation Predict Persistence in a Computer Science MOOC?

Cited by 36 publications

References 30 publications

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

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

Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

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