FieldAware Knowledge Tracing Machine by Modelling Students Dynamic Learning Procedure and Item Difficulty Knowledge tracing is essential for adaptive learning to obtain studentsu0027 current knowledge states to provide adaptive service. However, the learning process is evolving constantly since students learn and forget over time. Moreover, the difficulty of learning materials can also have huge influence on their performances. This work shows an ongoing project on knowledge tracing by proposing a FieldAware Knowledge Tracing Machine (FAKTM) to integrate studentsu0027 dynamic learning procedure (learning and forgetting) and adaptive item difficulty. We propose methods to model item difficulty and the dynamic learning procedure and present the framework to integrate them together. Preliminary analyses on a dataset show the promising results.

FeatureAware Knowledge Tracing for Generation of ConceptKnowledge Reports in an Intelligent Tutoring System In many Indian schools, a high studentteacher ratio makes it an uphill struggle for teachers to assess the knowledge of individual students and deficiencies in the studentsu0027 understanding. Teachers should have a clear picture on what concepts each student has mastered, and which concepts the teacher needs to review in greater detail. This paper investigates the studentsu0027 concept knowledge, based on the interaction of the students with an intelligent tutoring system. The FeatureAware Student knowledge Tracing (FAST) algorithm was used, since the algorithm facilitates the separation of lessonspecific skills from concept knowledge. Data from 2400 firstgrade students from 28 schools were used for the analysis. Findings include a moderate fit model and an easy interpretation of the model parameters.