Primary supervisor

Sulaiman Al Sheibani

Co-supervisors

Game-based learning is increasingly used in Computing education to encourage active participation, collaboration, problem-solving and application of technical and professional knowledge. Activities can range from low-cost classroom games such as Jeopardy-style quizzes, Bingo and team challenges to sophisticated digital simulations in which students make decisions and experience their consequences.

However, an important research challenge is understanding whether students who appear more engaged during games are actually learning more, and what patterns of interaction with games contribute to learning. Existing research suggests that gamification and game-based learning can positively affect motivation and learning, but the effects vary considerably according to game design, context and learner characteristics.

This project brings together Computing Education Research (CER), game-based learning and Learning Analytics (LA) to investigate how students learn through educational games. Rather than relying only on student satisfaction surveys, the project will explore learning processes through multiple sources of evidence, potentially including game interaction data, attempts, accuracy, decision pathways, time-on-task, participation patterns, learning activities, assessment performance and student reflections.

Different forms of games may be investigated, including Jeopardy-style activities, Bingo, team competitions, problem-solving challenges, scenario-based activities and digital serious games. One possible example is a Project Management simulation in which students manage time, budget, resources and project decisions. The project is not dependent on any single commercial game and aims to develop findings that can inform the design of game-based learning across Computing education.

The longer-term goal is to develop an evidence-informed understanding of which game designs work, for whom, under what circumstances, and how we can identify meaningful learning through learning analytics rather than equating engagement with enjoyment or activity alone.

Aim/outline

The overall aim of this project is to investigate how game-based learning and learning analytics can be combined to understand and improve student engagement and learning in Computing education.

The project will investigate questions such as:

  1. How do different game-based learning activities influence behavioural, cognitive, emotional and social engagement in Computing education?
  2. What learning analytics patterns can be identified from students' interactions with different types of educational games?
  3. Are particular patterns of game interaction associated with stronger learning outcomes?
  4. Which game mechanisms—such as competition, collaboration, challenge, choice, feedback and simulation—appear to support meaningful learning?
  5. Do different groups of learners interact with and benefit from game-based learning differently?
  6. How can game-based learning be designed to provide inclusive and accessible learning experiences?

The student researcher may:

  • conduct a literature review of game-based learning, gamification, serious games, Computing Education Research and Learning Analytics;
  • develop a taxonomy of game mechanics and their intended learning outcomes;
  • examine existing game-based activities used in Computing education;
  • assist in designing and evaluating educational games;
  • collect and/or analyse learning analytics data, subject to ethics and data availability;
  • analyse behavioural measures such as participation, attempts, response accuracy, time-on-task and interaction sequences;
  • combine learning analytics with survey, reflection, interview or assessment data;
  • explore statistical and/or visual analytics approaches for identifying patterns of engagement and learning;
  • investigate how game design influences different student groups;
  • develop recommendations for inclusive game-based learning in Computing education; and
  • contribute to the development of a research paper and/or an evidence-informed Game-Based Learning Analytics framework for Computing Education.

Depending on the student's interests and available data, the project may involve quantitative analysis, qualitative analysis, mixed methods, data visualisation or development of learning analytics prototypes.

Required knowledge

This project would suit a student interested in Computing Education, Learning Analytics, educational technology, data science, human-computer interaction or game-based learning.

Essential:

  • quantitative and/or qualitative research skills;
  • data analysis skills;
  • interest in understanding how students learn with technology; and
  • willingness to work with educational data.