Exploring Navigation Styles in a FutureLearn MOOC

Shi, Lei; Cristea, Alexandra I.; Toda, Armando M.; Oliveira, Wilk

doi:10.1007/978-3-030-49663-0_7

Cited by 7 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This finding is contrary to the early claim that MOOCs are a “disruptive innovation” to the higher education system (Yuan & Powell, 2013) and consistent with “a growing consensus that MOOCs will be integrated into existing higher education systems” (Littenberg‐Tobias & Reich, 2020, p.1). Unlike the previous studies, we did not find learners registering for this MOOC for social reasons reported by Shi et al (2020), nor for the certificate of completion by Semenova (2020).…”

Section: Resultscontrasting

confidence: 99%

The role of gender and employment status in MOOC learning: An exploratory study

Dai

Teo

Rappa

2022

Computer Assisted Learning

View full text Add to dashboard Cite

Background: Learners in a given massive open online course (MOOC) are usually provided with the same learning materials, guided by the same syllabus, and assessed in the same format. This "one-size-fits-all" approach constrains learners' ability to reap the optimal benefits from online learning.Objectives: This study aims to characterize learners' differences in MOOC learning. Specifically, it examines how learners might vary in their enrolment motivation and the development of continuance intention to learn in a MOOC because of their gender and employment status.Methods: Data were collected via a questionnaire survey. Quantitative and qualitative methods were used to analyse data from 664 learners in a Chinese MOOC. Results and Conclusion:The research revealed significant differences in learners' enrolment motivation across groups defined by employment status, but not for gender groups. Learner groups defined by gender and employment status experienced variant psychological processes when deciding to continue to learn in the MOOC.Major Takeaways: Working adults stressed the instrumental values derived from MOOC learning; therefore, it is vital to design and integrate additional features into the MOOC to satisfy their needs. Besides, it would be critical to understand female learners' and working adults' expectations of MOOC learning, as they are more sensitive to confirmation in determining their attitudes toward learning in a MOOC. A short pre-course survey of learners' expectations would serve the purpose.

show abstract

Section: Resultscontrasting

confidence: 99%

The role of gender and employment status in MOOC learning: An exploratory study

Dai

Teo

Rappa

2022

Computer Assisted Learning

View full text Add to dashboard Cite

show abstract

“…Seven programming features on the platform were identified: Average number of codes (ACo), Average number of changes (ACh), Number of platform operations (NPo), Number of clicks on debug (ND), Number of syntactical errors (NSe), Average time between two debugs (AtD), and Average time on irrelevant behaviour (AtIb) (see Table 2). Moreover, clustering analysis (CA) was used to uncover hidden patterns in a complex dataset, and many works used these unsupervised learning methods to analyse new relationships in educational data (Dutt et al, 2015; Shi et al, 2019; Shi & Cristea, 2018). We clustered learners based on the learners' logs, to inspect the patterns of programming features in each learner cluster.…”

Section: Methodsmentioning

confidence: 99%

Using multimodal learning analytics to understand effects of block‐based and text‐based modalities on computer programming

Sun,

Ouyang,

et al. 2024

Computer Assisted Learning

View full text Add to dashboard Cite

BackgroundWith the development of computational literacy, there has been a surge in both research and practice application of text‐based and block‐based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings.ObjectivesTo further compare programming effects between and within text‐based and block‐based modalities, this research conducted a quasi‐experimental research in China's secondary school.MethodsAn online programming platform, Code4all, was developed to allow learners to program in text‐based and block‐based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag‐sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities.Results and ConclusionsThe results indicated that learners in text‐based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block‐based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns.ImplicationsThree major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process‐oriented and summative analysis to reveal learners' programming learning quality.

show abstract

“…As the traditional one-size-fits-all approach can no longer satisfy student learning needs, it leads to increased demands for customised learning [27,28]. Various student modelling methods have been proposed, which are generally classified as integrating expert knowledge-based or data-driven methods [29,30]. Knowledge-based methods refer to utilising human knowledge to address issues that would normally require human intelligence [7,31].…”

Section: Student Modellingmentioning

confidence: 99%

Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems

Li,

Shi,

Wang

et al. 2023

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

The continuous application of artificial intelligence (AI) technologies in online education has led to significant progress, especially in the field of Intelligent Tutoring Systems (ITS), online courses and learning management systems (LMS). An important research direction of the field is to provide students with customised learning trajectories via student modelling. Previous studies have shown that customisation of learning trajectories could effectively improve students’ learning experiences and outcomes. However, training an ITS that can customise students’ learning trajectories suffers from cold-start, time-consumption, human labour-intensity, and cost problems. One feasible approach is to simulate real students’ behaviour trajectories through algorithms, to generate data that could be used to train the ITS. Nonetheless, implementing high-accuracy student modelling methods that effectively address these issues remains an ongoing challenge. Traditional simulation methods, in particular, encounter difficulties in ensuring the quality and diversity of the generated data, thereby limiting their capacity to provide intelligent tutoring systems (ITS) with high-fidelity and diverse training data. We thus propose Sim-GAIL, a novel student modelling method based on generative adversarial imitation learning (GAIL). To the best of our knowledge, it is the first method using GAIL to address the challenge of lacking training data, resulting from the issues mentioned above. We analyse and compare the performance of Sim-GAIL with two traditional Reinforcement Learning-based and Imitation Learning-based methods using action distribution evaluation, cumulative reward evaluation, and offline-policy evaluation. The experiments demonstrate that our method outperforms traditional ones on most metrics. Moreover, we apply our method to a domain plagued by the cold-start problem, knowledge tracing (KT), and the results show that our novel method could effectively improve the KT model’s prediction accuracy in a cold-start scenario.

show abstract

Exploring Navigation Styles in a FutureLearn MOOC

Cited by 7 publications

References 13 publications

The role of gender and employment status in MOOC learning: An exploratory study

The role of gender and employment status in MOOC learning: An exploratory study

Using multimodal learning analytics to understand effects of block‐based and text‐based modalities on computer programming

Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems

Contact Info

Product

Resources

About