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Abstract.Using hidden Markov models (HMMs) and traditional behavior analysis, we have examined the effect of metacognitive prompting on students' learning in the context of our computer-based learning-by-teaching environment. This paper discusses our analysis techniques, and presents evidence that HMMs can be used to effectively determine students' pattern of activities. The results indicate clear differences between different interventions, and links between students learning performance and their interactions with the system.

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In this paper, we describe our approach to the households recommendation track of Challenge on Context-Aware Movie Recommendation(CAMRa) 2011. The challenge of the track is to generate recommendations for households. In this paper, we introduce an approach that uses time information and exponential smoothing to predict user and item ratings. We provide traditional collaborative filtering algorithms as a baseline, and show that our approach yields better results.

In this paper, we describe a task-based method to evaluate relative effectiveness of Wikipedia. We then use this method to compare Wikipedia against an internet search engine (Google) and an answer engine that uses structured data (Wolfram Alpha).

This paper develops and evaluates an approach for combining semantic information with proximity information for text summarization. The approach is based on the proximity language model, which incorporates proximity information into the unigram language model. This paper novelly expands the proximity language model to also incorporate semantic information using latent semantic analysis (LSA). We argue that this approach achieves a good balance between syntactic and semantic information. We evaluate the approach using ROUGE scores on the Text Analysis Conference (TAC) 2009 Summarization task, and find that incorporating LSA into PLM gives improvements over the baseline models. 3 PROXIMITY LANGUAGE MODEL Proximity language model (PLM) forms the heart of our ranking function, and is based on the unigram language model (Zhao and Yun, 2009).