Objective: The goal of this study was to evaluate two kinds of difficulty adaptation techniques in terms of enjoyment and performance in a simple memory training game: one based on difficulty-performance matching (“task-guided”) and the other based on providing a high degree of control/choice (“user-guided”).
Furthermore, using SNA to monitor and improve collaborative learning could contribute to better learning and teaching. The early prediction of performance offers a clear opportunity for the implementation of effective remedial strategies and facilitates improvements in learning. The combination of SNA visualization and mathematical analysis of students' position, connectedness, and role in collaboration was found to help predict students' performance with reasonable accuracy. SNA-based monitoring helped improve collaborative learning through a data-driven intervention. Visualizing collaborative learning interactions using SNA offered an easy-to-interpret overview of the status of collaboration, and mapped the roles played by teachers and students.
The results indicate that a reasonable number of underachieving students could be predicted early using self-regulation, engagement, and collaborative learning indicators. Using insights from these steps, different statistical and machine learning methods were applied to analyze the data. Exploratory statistical analysis was conducted to explore trends and potential predictors, and to guide the selection of analysis methods. Descriptive statistics and visualizations were performed to summarize the data, plot variables' distributions, and detect interesting patterns. Data from different sources were assembled and verified, and corrupted records were eliminated. Students' data were collected from the online learning management system using custom plugins and database queries. The research methods followed a structured process of data collection, preparation, exploration, and analysis. More specifically, the research questions comprise: How can learning analytics and social network analysis (SNA) reliably predict students' performance using contextual, theory-based indicators, and how can social network analysis be used to analyze online collaborative learning, guide a data-driven intervention, and evaluate it. The prime objective of this thesis is to investigate the potential of learning analytics in understanding learning patterns and learners' behavior in collaborative learning environments with the premise of improving teaching and learning. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. If you wish to cancel your subscription, please click here.Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. My subscription can be cancelled at any time.
#Pdfzone in trial#
I hereby authorise PDFZone to deduct the monthly subscription amount from the bank card details used to purchase the trial offer, recurring monthly. My monthly subscription will be automatically confirmed and renewed on a monthly basis. If you wish to cancel your subscription, please click here.īy validating this information, I confirm that I am the cardholder (or authorised to use the payment details provided above) and that I confirm my order for an unlimited monthly subscription priced at €29.90 (inclusive of tax). I declare that I have read and accepted the T&Cs.īy validating this information, I confirm that I am the cardholder (or authorised to use the payment details provided above) and that I confirm my order for an unlimited monthly subscription following expiration of the trial period.
0 kommentar(er)
