scholarly journals Dropout Time and Learners’ Performance in Computer Programming MOOCs

2021 ◽  
Vol 11 (10) ◽  
pp. 643
Author(s):  
Marili Rõõm ◽  
Marina Lepp ◽  
Piret Luik
Keyword(s):  
Quantitative Study ◽  
Computer Programming ◽  
Online Courses ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Dropping Out ◽  
Massive Open Online ◽  
High Dropout Rate ◽  
Different Levels

One of the problems regarding MOOCs (Massive Open Online Courses) is the high dropout rate. Although dropout periods have been studied, there is still a lack of understanding of how dropout differs for MOOCs with different levels of difficulty. A quantitative study was conducted to determine the periods with the highest dropouts in computer programming MOOCs and the performance of the dropouts on the course before dropping out. Four occurrences of three MOOCs, with different durations, difficulty of the topic, and the degree of supportive methods, were included. The results showed that dropout was highest at the beginning of all studied courses. Learners also dropped out before the project. In the easier and shorter courses, most dropouts were successful until they quit the course. In longer and more difficult courses, learners mainly dropped out in the week they started due to experiencing problems with the course activities. It is suggested to recommend that learners take courses at a level that suits them if their current course is too easy or difficult and encourage learners to use course resources for help. It would be a good idea to provide learners with example topics to assist them in starting with a project.

scholarly journals A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs

2021 ◽  
Vol 16 (23) ◽  
pp. 216-232
Author(s):  
Khaoula Mrhar ◽  
Lamia Benhiba ◽  
Samir Bourekkache ◽  
Mounia Abik
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Dropping Out ◽  
Bayesian Neural Network ◽  
Massive Open Online ◽  
Collaborative Tools ◽  
Novel Approach ◽  
Analysis Task

Massive Open Online Courses (MOOCs) are increasingly used by learn-ers to acquire knowledge and develop new skills. MOOCs provide a trove of data that can be leveraged to better assist learners, including behavioral data from built-in collaborative tools such as discussion boards and course wikis. Data tracing social interactions among learners are especially inter-esting as their analyses help improve MOOCs’ effectiveness. We particular-ly perform sentiment analysis on such data to predict learners at risk of dropping out, measure the success of the MOOC, and personalize the MOOC according to a learner’s behavior and detected emotions. In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM. To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task. Our em-pirical results indicate that: 1) The Bayesian CNN-LSTM model provides interesting performance compared to other models (CNN-LSTM, CNN, LSTM) in terms of accuracy, precision, recall, and F1-Score; and 2) there is a high correlation between the sentiment in forum posts and the dropout rate in MOOCs.

scholarly journals A Grounded Theory Exploration of Language Massive Open Online Courses (LMOOCs): Understanding Students’ Viewpoints

2021 ◽  
Vol 13 (5) ◽  
pp. 2577
Author(s):  
Robert Li-Wei Hsu
Keyword(s):  
Grounded Theory ◽  
Theoretical Model ◽  
Online Courses ◽  
Internet Access ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
English For Specific Purposes ◽  
Massive Open Online ◽  
Innovation In Education ◽  
High Dropout Rate

Massive open online courses (MOOCs) have been called the biggest innovation in education in 200 years for their unique attribute of being open and free to any individual with Internet access; however, their high dropout rate has led many people to be concerned or dubious about their effectiveness and applicability. The applicability of MOOCs in English for specific purposes (ESP) courses (in this case, hospitality English) needs more empirical evidence; the present study intends to help fill this gap and extend our current understanding of this issue. This study followed a grounded theory methodology to develop a theoretical model based on a constant dialogue between collected data and the literature. The results suggest that most participants had positive perceptions of language MOOCs (LMOOCs) in general, but some doubted their applicability. Most participants said they would continue to use LMOOCs for learning, depending on the attributes of specific courses. Based on the extracted data, a conceptual model for the applicability of LMOOCs is proposed.

scholarly journals Gamification Increases Completion Rates in Massive Open Online Courses

2022 ◽  
Vol 18 (1) ◽  
pp. 0-0
Keyword(s):  
Online Courses ◽  
Course Completion ◽  
Dropout Rate ◽  
Completion Rate ◽  
Massive Open Online Courses ◽  
Dropout Rates ◽  
Completion Rates ◽  
Massive Open Online ◽  
High Dropout Rate ◽  
Course Completion Rate

Massive Open Online Courses (MOOCs) aim at unlimited participation and open access via the web. There are concerns about the actual value of such courses. This is predominantly due to higher dropout rates. According to studies, only 7-13% go on to complete these courses. The high dropout rate in MOOCs is a challenge for education providers. This paper aims to explore reasons for high dropout rates within MOOCs and how they can be minimized. With this in mind, two research questions have been set for this study: 1) Why do MOOC participants not complete their courses? 2) How can the course completion rate be increased? Implementation of the strategies investigated in this paper can increase completion rates in MOOCs. In conclusion, after analyzing the collected data, the final results have shown that gamification increased the completion rate of MOOCs.

scholarly journals A grounded theory exploration of language massive open online courses (LMOOCs): Understanding students’ viewpoints

2020 ◽  
Author(s):  
Liwei Hsu
Keyword(s):  
Grounded Theory ◽  
Theoretical Model ◽  
Empirical Evidence ◽  
Online Courses ◽  
Internet Access ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
English For Specific Purposes ◽  
Massive Open Online ◽  
High Dropout Rate

Abstract Massive open online courses (MOOCs) have been called the biggest innovation to happen in education for 200 years for their unique attribute of being open and free to any individual with Internet access; however, their high dropout rate has led many people to be concerned or dubious about MOOCs’ effectiveness and applicability. The applicability of MOOCs in English for specific purposes (ESP) courses (in this case, Hospitality English) needs more empirical evidence; the present study intends to help fill this gap and extend our current understanding of this issue. This study followed a grounded theory methodology to develop a theoretical model based on constant dialogue between collected data and the literature. The results suggest that most participants had positive perceptions of LMOOCs in general but that some of them doubted the applicability of LMOOCs. Most participants said they would continue to use LMOOCs in learning, depending on the attributes of specific courses. Based on the extracted data, a conceptual model for the applicability of LMOOCs is proposed.

scholarly journals A Grounded Theory Exploration of Language Massive Open Online Courses (LMOOCs): Understanding Students’ Viewpoints

2020 ◽  
Author(s):  
Liwei Hsu
Keyword(s):  
Grounded Theory ◽  
Theoretical Model ◽  
Empirical Evidence ◽  
Online Courses ◽  
Internet Access ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
English For Specific Purposes ◽  
Massive Open Online ◽  
High Dropout Rate

Abstract Massive open online courses (MOOCs) have been called the biggest innovation to happen in education for 200 years for their unique attribute of being open and free to any individual with Internet access; however, their high dropout rate has led many people to be concerned or dubious about MOOCs’ effectiveness and applicability. The applicability of MOOCs in English for specific purposes (ESP) courses (in this case, Hospitality English) needs more empirical evidence; the present study intends to help fill this gap and extend our current understanding of this issue. This study followed a grounded theory methodology to develop a theoretical model based on constant dialogue between collected data and the literature. The results suggest that most participants had positive perceptions of LMOOCs in general but that some of them doubted the applicability of LMOOCs. Most participants said they would continue to use LMOOCs in learning, depending on the attributes of specific courses. Based on the extracted data, a conceptual model for the applicability of LMOOCs is proposed.

scholarly journals MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Chen ◽  
Jun Feng ◽  
Xia Sun ◽  
Nannan Wu ◽  
Zhengzheng Yang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Extreme Learning Machine ◽  
Hybrid Algorithm ◽  
Online Courses ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Wide Range ◽  
Good Classification ◽  
High Dropout Rate ◽  
Learning Machine

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.

scholarly journals The Effect of Non-Monetary Rewards on Employee Performance in Massive Open Online Courses

2020 ◽  
Vol 15 (01) ◽  
pp. 88 ◽  
Author(s):  
Pradorn Sureephong ◽  
Winai Dahlan ◽  
Suepphong Chernbumroong ◽  
Yootthapong Tongpaeng
Keyword(s):  
Online Courses ◽  
Positive Impact ◽  
Employee Performance ◽  
Online Training ◽  
Employee Motivation ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Science Center ◽  
Massive Open Online ◽  
Monetary Rewards

A challenge for organizations is to increase employee performance and motivation, since the most crucial asset of every organization is manpower. Many companies and factories have started implementing online training platforms under the concept of “Massive Open Online Courses (MOOCs)” in their workplace to foster employee performance. Previously, the mobile application called “HSC MOOC” which is provided by the Halal Science Center, Chulalongkorn Univer-sity, Thailand functioned as a solution that encourages self-learning on online platforms at companies in Thailand. However, the main barrier or risk that occurs when implementing an online platform is the user’s motivation, since the dropout rate is considered as a serious issue regarding MOOCs. Thus, incentive and re-ward were added to online training programs which aimed to motivate employees. Many types of rewards were provided for employees who had met their own company’s expectations. Recently, psychology research papers have illustrated that non-monetary rewards seem to provide greater results on the side of employee’s motivation. However, not all types of non-monetary rewards provide positive impact on employee’s motivation. Therefore, the aim of this research is to present the effect of different non-monetary rewards on employee performance. Ninety volunteer employees from a food manufacturing company in Chiang Mai, Thailand participated in this research. The experiment was divided into two sections. The first section aimed to measure the motivation of employees which based on different non-monetary rewards. The questionnaire for measuring Valence, Instrumentality, and Expectancy variables (VIE theory) was deployed to test employee motivation in 3 different groups; “Tangible Non-Monetary Re-wards”, “Social Non-Monetary Rewards” and “Job Related Non-Monetary Re-wards”. The test consisted of 10 items using a 5-point Likert scale. The second experiment aimed to reveal which type of non-monetary reward is the most suitable for motivating employees in participating and completing the course in MOOCs. Participants in different groups were assigned to learn via MOOCs on their mobile device within a period of 30 days. Different types of non-monetary rewards were provided only for participants who had completed certain conditions in MOOCs. The overall results showed that the group of tangible non-monetary rewards reached the significant highest score on the VIE questionnaire and over 60% of participants exposed to tangible non-monetary rewards completed the course’s conditions in MOOCs.

scholarly journals Retention in MOOCS: some key factors

2020 ◽  
Author(s):  
Andrés Chiappe ◽  
Blanca Diana Lorena Castillo
Keyword(s):  
Online Courses ◽  
Massive Open Online Courses ◽  
Dropping Out ◽  
Review Of Literature ◽  
Key Factors ◽  
Massive Open Online ◽  
Educational Trend ◽  
The Subject ◽  
Main Factors

Abstract The dropping out of students in the Massive Open Online Courses (Mooc) has been the subject of debate and concern on the part of educational researchers and practitioners during the last decade. Considering its growth as an educational trend and the emerging research generated on this topic, a systematic review of literature on 131 studies was conducted about attrition in both cMooc and xMoocs. The results highlight the role of collaboration, the sense of community, the need for certification and standardization as the main factors that affect attrition in Moocs.

scholarly journals Design a Computer Programming Learning Environment for Massive Open Online Courses

2015 ◽  
pp. 255-274 ◽  
Author(s):  
Ricardo Queirós
Keyword(s):  
Learning Environment ◽  
Interface Design ◽  
Teaching And Learning ◽  
Computer Programming ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Design Model ◽  
Massive Open Online ◽  
Immediate Feedback ◽  
Central Piece

Teaching and learning computer programming is as challenging as it is difficult. Assessing the work of students and providing individualised feedback is time-consuming and error prone for teachers and frequently involves a time delay. The existent tools prove to be insufficient in domains where there is a greater need to practice. At the same time, Massive Open Online Courses (MOOC) are appearing, revealing a new way of learning. However, this paradigm raises serious questions regarding the monitoring of student progress and its timely feedback. This chapter provides a conceptual design model for a computer programming learning environment. It uses the portal interface design model, gathering information from a network of services such as repositories, program evaluators, and learning management systems, a central piece in the MOOC realm. This model is not limited to the domain of computer programming and can be adapted to any area that requires evaluation with immediate feedback.

scholarly journals Massive Open Online Courses (MOOCs) Dropout Rate in the World: A Systematic Review Protocol

2020 ◽  
Author(s):  
Manoosh Mehrabi ◽  
Ali Reza Safarpour ◽  
Abbas Ali Keshtkar
Keyword(s):  
Systematic Review ◽  
Online Courses ◽  
Meta Analysis ◽  
Dropout Rate ◽  
Open Science ◽  
Massive Open Online Courses ◽  
Massive Open Online ◽  
The World ◽  
Review Protocol ◽  
Science Framework

Abstract BackgroundRecently, massive open online courses (MOOCs) have received increasing popularity throughout the world. Regardless of the subject taught and the university providing the course, the dropout rate of MOOCs is one of the most important challenges ahead.Methods This systematic review will search MEDLINE/PubMed, Scopus, Web of Science (Clarivate Analytics), Embase (Embase.com), ASSIA, CINAHL, Education Research, BEI, and Eric databases systematically according to predefined criteria without language restrictions to retrieve prospective and retrospective observational studies conducted between the 1st of January 2000 and 30th of March 2020 which evaluated the frequency of leaving MOOCs throughout the world. In the absence of severe methodological heterogeneity, the data will be combined and a meta-analysis will be performed. DiscussionAs dropout rate is one of the most challenges that universities may encounter, this systematic review will help universities extend their view, save their resources or maybe design their MOOCs differently.RegistrationRegistered in Open Science Framework, available at: https://osf.io/jgyqx/

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