The doctoral dissertation in the field of Computer Science will be examined at the Faculty of Science, Forestry and Technology, Joensuu campus and online.
What is the topic of your doctoral research? Why is it important to study the topic?
Collaborative learning encourages critical skills such as teamwork, communication, and problem-solving. Understanding the dynamics of collaborative learning helps educators to design more effective teaching strategies to improve student engagement and learning outcomes. Leveraging learning analytics provides insights into student behavior and learning processes and enables personalized and adaptive learning.
The research introduces and validates new methodologies, such as combining social network analysis, epistemic network analysis, sequence mining, and process mining, which were applied to have a comprehensive understanding of collaborative learning.
What are the key findings or observations of your doctoral research?
The finding from the research opened up possibilities regarding the targeted interventions needed to enhance the quality of collaborative learning experiences and promote better students’ academic performance. The study of the dynamics of students' interactions may allow instructors to design activities that more appropriately scaffold student learning, enabling students to engage more intensely in activities.
My research offers several new and valuable contributions to the field of collaborative learning. The integration of multiple analytical methods provides a comprehensive and nuanced understanding of both the social and temporal dimensions of student interactions. The longitudinal approach of tracking student interactions over an entire academic year reveals how roles and interaction patterns evolve, which is important to design effective interventions to support students. The findings emphasize the importance of social interactions to design better group formation and have better learning outcomes. The research extends theoretical frameworks by incorporating temporal dynamics, challenging traditional theories that focus on the content and structure of interactions, and advocating for a more dynamic and evolving perspective.
How can the results of your doctoral research be utilised in practice?
Predicting students’ roles in collaborative learning could help the teacher to tailor tasks based on students’ strengths and work on their weaknesses. Teachers can encourage students to go beyond their comfort zones while striking a balance between assigning roles and offering students opportunities to explore and develop new social and cognitive skills. The positive correlation between students’ interactions and academic performance highlights the need for strategies to maintain active participation. Teachers can ensure students’ activity by designing activities that give students various responsibilities and support the struggles that students face in collaborative learning.
What are the key research methods and materials used in your doctoral research?
I used a combination of qualitative and quantitative methods to gain a comprehensive understanding of computer-supportive collaborative learning by collecting data from online problem-based learning discussions. I analyzed the student interactions using different learning analytics methods such as social network analysis, epistemic network analysis for capturing the social dimension, and sequence mining and process mining to capture the dynamics of students' interaction.
The doctoral dissertation of Ramy Elmoazen, MSc, entitled Using learning analytics to capture social and temporal dimensions of collaborative learning willbe examined at the Faculty of Science, Forestry and Technology, Joensuu Campus. The opponent will be Research Fellow Melissa Bond, University College London, and the custos will be Associate Professor Mohammed Saqr, University of Eastern Finland. Language of the public defence is English.
