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Outputs (3)

Utilizing Massive Spatiotemporal Samples for Efficient and Accurate Trajectory Prediction (2013)
Journal Article
Chan, A., & Li, F. W. (2013). Utilizing Massive Spatiotemporal Samples for Efficient and Accurate Trajectory Prediction. IEEE Transactions on Mobile Computing, 12(12), 2346-2359. https://doi.org/10.1109/tmc.2012.214

Trajectory prediction is widespread in mobile computing, and helps support wireless network operation, location-based services, and applications in pervasive computing. However, most prediction methods are based on very coarse geometric information s... Read More about Utilizing Massive Spatiotemporal Samples for Efficient and Accurate Trajectory Prediction.

A Fine-Grained Outcome-Based Learning Path Model (2013)
Journal Article
Yang, F., Li, F. W., & Lau, R. W. (2014). A Fine-Grained Outcome-Based Learning Path Model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(2), 235-245. https://doi.org/10.1109/tsmcc.2013.2263133

A learning path (or curriculum sequence) comprises steps for guiding a student to effectively build up knowledge and skills. Assessment is usually incorporated at each step for evaluating student learning progress. SCORM and IMS-LD have been establis... Read More about A Fine-Grained Outcome-Based Learning Path Model.

Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior (2013)
Presentation / Conference Contribution
Watson, C., Li, F. W., & Godwin, J. L. (2013). Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior. In Proceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT 2013) (319-323). https://doi.org/10.1109/icalt.2013.99

The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognit... Read More about Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior.