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Aied 2013


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AIED 2013 (Poster)

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Aied 2013

  1. 1. Intelligent Systems ProgramUniversity of Pittsburgh Problem Definition o Concept-level adaptive sequencing is a relatively mature and well-known approach; however, there are still no instances of its use in the context of exam preparation. o Instead of gradual coverage of concepts, exam-time sequencing should focus on bridging knowledge gaps while trying to maximize the number of concepts that are assessed and mastered by completing each suggested problem. Knowledge Maximizer o Knowledge Maximizer (KM) is a concept-based problem sequencing tool for Java programming exam preparation. o Goal : providing a sequence of questions to help learner address gaps in Java knowledge as quickly as possible. o Learning content: 103 parameterized self-assessment questions (activity) indexed by ontology concepts with prerequisite and outcomes concepts. o KM uses an overlay student model in conjunction with a concept-level model of Java knowledge represented in the form of Java ontology. ∑ ∑ ′ ′ = r r M i iw M i iwik K ∑ ∑ ′ ′− = o o M i i M i ii w wk I )1( 1 1 + −= t s S Evaluation o A classroom study conducted for Java-based undergraduate course. o KZ was used for final exam preparation, in addition to QuizGuide (QG), and Progressor+ (P+), which were available from the beginning of the semester for accessing java questions. o 14 students had attempts from KM while 17 students had attempts from QG and P+. Despite a remarkable increase in complex questions in KM, the success rates across all systems were comparable Method How prepared is the student to do the activity? More knowledge of prerequisite concepts (K) for an activity makes it a better candidate for selection by the optimizer. Mr : prerequisite concepts for the activity wi’: log-smoothed weight for the concept ki: level of the learner’s knowledge in i-th concept What is the impact of the activity? Impact of a certain activity (I) measures how well it addresses the current lack of knowledge. An activity with a higher impact is a better candidate for selection by the optimizer. Mo: outcome concepts for the activity Has the user already completed the activity? Inverse of success rate measures the extent to which the user needs to repeat the activity. s : number of the success in activity; t: total number of attempts in activity Activity Rank : )(S SIKR ++= -12.80% -13.70% 11.53% -15.00% -10.00% -5.00% 0.00% 5.00% 10.00% 15.00% Class QG/P+ KM Average Relative Progress Percentage o KM pushes students toward appropriate complex questions which targets more concepts at once. o This helps students fill the gaps in their knowledge in a more efficient way.6.20% 43.50% 50.20% 34.60% 45.30% 20.10% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Easy Moderate Complex Attempts per Question Complexity KM QG,P+ 58% 64% 0% 20% 40% 60% 80% 100% KM QG,P+ Success rate