Research Analysis on Mooc Course Dropout and Retention Rates

Gomez-Zermeno, Marcela Gerogina; Garza, Lorena Yadira Alemán de la

doi:10.17718/tojde.23429

Cited by 27 publications

(27 citation statements)

References 3 publications

(5 reference statements)

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“…Henek (3) in his study stated that on average, only 10% of students completed a course. This report is similar to Gomez-Zermeno and De La Garza (4) who found that 86% of their study sample were not retained in MOOCs. The low retention is caused by many factors including interaction, which plays a great role.…”

Section: Introductionsupporting

confidence: 89%

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

Mohamad¹,

Ahmad²,

Jawawi³

2018

IJET

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Massive Open Online Courses MOOCs have become more acceptable as a learning program globally, including Malaysia. One main issue that has been discussed since the implementation of MOOCs is the issue of low student retention or high dropout rates from the course. Various factors have been found to play a role in this issue including the interaction factor. Previous studies have experimented with various strategies to monitor student retention and apply intervention programs to improve the situation. The strategies include the usage of machine learning and data mining techniques in analysing students’ online interactions to predict student retention rates. The implementation of these strategies produced promising result. However, in Malaysia, these strategies are not really implemented yet. Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs. The proposed method includes predictive analytics that involves classification analysis. This paper suggests that the method can be applied to the current platform and complement intervention programs for the issue of low retention or high dropouts with several improvements.

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Section: Introductionsupporting

confidence: 89%

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

Mohamad¹,

Ahmad²,

Jawawi³

2018

IJET

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“…Dropouts and early abandonment in e-learning are important concerns for the current education industry [64]. Despite the great effort of companies and education centers to reduce these negative aspects by designing engaging and motivational distance and digital courses and applications, these problems have not yet been solved [65].…”

Section: B Learning Digital Games and Gamification On Computer-assismentioning

confidence: 99%

Using Challenges to Enhance a Learning Game for Pronunciation Training of English as a Second Language

et al. 2020

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Learning games have a remarkable potential for education. They provide an emergent form of social participation that deserves the assessment of their usefulness and efficiency in learning processes. This study describes a novel learning game for foreign pronunciation training in which players can challenge each other. Native Spanish speakers performed several pronunciation activities during a one-month competition using a mobile application, designed under a minimal pairs approach, to improve their pronunciation of English as a foreign language. This game took place in a competitive scenario in which students had to challenge other participants in order to get high scores and climb up a leaderboard. Results show intense practice supported by a significant number of activities and playing regularity, so the most active and motivated players in the competition achieved significant pronunciation improvement results. The integration of automatic speech recognition (ASR) and text-to-speech (TTS) technology allowed users to improve their pronunciation while being immersed in a highly motivational game. INDEX TERMS Computer-assisted pronunciation training, mobile learning game, mobile application, English L2 pronunciation, challenges, motivation.

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“…Furthermore, we can report a substantially higher completion rate than the earlier analyses. More than 60% of registered participants completed the course under study, as opposed to dropout rates in the order of 86% (Gomez-Zermeno & Aleman, 2016) or 90% (Hew & Cheung, 2014). Therefore, in line with Brooker et al (2018), we believe we need to understand how different types and levels of motivation relate to those variables that guarantee students a successful learning experience.…”

Section: Introductionmentioning

confidence: 99%

Exploring how student motivation relates to acceptance and participation in MOOCs

Frías

Montaño

Barrio‐García

2020

Interactive Learning Environments

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In recent years, MOOCs have become firmly established as valid e-learning environments and, as such, have been developed by many universities using different types of platform. Given the voluntary nature of MOOC enrollment, motivation is crucial to our understanding of why students register for and complete these courses. The present study explores the motivations that characterize MOOC participants and how they relate to technology acceptance variables (data collected via questionnaires) and participation variables (observational data collected via the platform). Our results indicate that students show exceptionally high levels of intrinsic motivation. However, extrinsic motivation also plays a relevant role, suggesting that the two are not mutually exclusive. Although only intrinsic motivation appears to be systematically associated with differences in technology acceptance, both are associated with differences in participation, but in contrasting ways. Our results provide insights that will enable us to improve MOOC design in order to enhance participant satisfaction, particularly when different sources of motivation are involved. Future research based on the modeling of technology acceptance and participation will also benefit from this study.

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Research Analysis on Mooc Course Dropout and Retention Rates

Cited by 27 publications

References 3 publications

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

Using Challenges to Enhance a Learning Game for Pronunciation Training of English as a Second Language

Exploring how student motivation relates to acceptance and participation in MOOCs

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