Designing Adaptive Engagement Approaches for Audience-bounded Online Communities

Audience-bounded online communities require innovative user engagement techniques. Without special efforts focus- ing on engagement, the contribution volume will likely to be insufficient to maintain a sustainable community-driven sys- tem. This paper presents an adaptive approach for user en- gagement that aims to apply alternative engagement strate- gies to users with different behavior in the online community. We report the results of the first experiments testing the fea- sibility of such approach. We discuss further design options that can be explored and the implications of the approach

Addictive links: The motivational value of adaptive link annotation

Adaptive link annotation is a popular adaptive navigation support technology. Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. In this paper, we present our exploration of a lesser known effect of adaptive annotation, its ability to significantly increase students' motivation to work with non-mandatory educational content. We explored this effect and confirmed its significance in the context of two different adaptive hypermedia systems. The paper presents and discusses the results of our work

Final report, independent Study during Fall 2009 "Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles"

This report describes our study of different ways to improve existing collaborative filtering techniques in order to recommend scientific articles. Using data crawled from CiteUlike, a collaborative tagging service for academic purposes, we compared the classical user-based collaborative filtering algorithm as described by Schafer et al. [2], with two enhanced variations: 1) using a tag-based similarity calculation, to avoid depending on ratings to find the neighborhood of a user, and 2) incorporate the amount of raters in the final recommendation ranking to decrease the noise of items that have been rated by too few users. We provide a discussion of our results, describing the dataset and highlighting our findings about applying collaborative filtering on folksonomies instead of the classic bipartite user-item network, and providing guidelines of our future research

Domain Modeling for Personalized Guidance

This chapter attempts to untangle the relationships between personalized guidance and domain modeling, as well as to explain how domain modeling could be used to provide personalized guidance. The problem of personalized guidance has a long history in the area of adaptive educational systems (AES). In fact, the very first recognized AES SCHOLAR (Carbonell, 1970) focused on guiding students to the most relevant facts and questions about the geography of South America. The SCHOLAR functionality was based on a domain model in the form of a semantic network and an overlay student model. Since that time, a considerable share of research in the field of AES has focused on different kinds of personalized guidance, and the majority of this work relied heavily on domain modeling—which makes these two research directions heavily interconnected

Adaptive Information Visualization for Personalized Access to Educational Digital Libraries

Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page

Analyzing User Behavior Patterns in Adaptive Exploratory Search Systems with LifeFlow

Adaptive exploratory search is a method that can provide user-centered personalized search results by incorporating interactive user interfaces. Analyzing the user behavior pat- terns of these systems can be complicated when they sup- port transparent and controllable open user models. This paper suggests to use a visualization tool to address the problem, as a complement to the typical statistical analy- sis. By adopting an event sequence visualization tool called LifeFlow, we were able to easily find out user interesting behavior patterns, especially regarding the open user model exploration

FAST: Feature-Aware Student Knowledge Tracing

Various kinds of e-learning systems, such as Massively Open Online Courses and intelligent tutoring systems, are now producing amounts of feature-rich data from students solving items at different levels of proficiency over time. To analyze such data, researchers often use Knowledge Tracing [4], a 20-year old method that has become the de-facto standard for inferring student’s knowledge from performance data. Knowledge Tracing uses Hidden Markov Models (HMM) to estimate the latent cognitive state (student’s knowledge) from the student’s performance answering items. Since the original Knowledge Tracing formulation does not allow to model general features, a considerable amount of research has focused on ad-hoc modifications to the Knowledge Tracing algorithm to enable modeling a specific feature of interest. This has led to a plethora of different Knowledge Tracing reformulations for very specific purposes. For example, Pardos et al. [5] proposed a new model to measure the effect of students’ individual characteristics, Beck et al. [2] modified Knowledge Tracing to assess the effect of help in a tutor system, and Xu and Mostow [7] proposed a new model that allows measuring the effect of subskills. These ad hoc models are successful for their own specific purpose, but they do not generalize to arbitrary features. Other student modeling methods which allow more flexible features have been proposed. For example, Performance Factor Analysis [6] uses logistic regression to model arbitrary features, but unfortunately it does not make inferences of whether the student has learned a skill. We present FAST (Feature-Aware Student knowledge Tracing), a novel method that allows general features into Knowledge Tracing. FAST combines Performance Factor Analysis (logistic regression) with Knowledge Tracing, by leveraging on previous work on unsupervised learning with features [3]. Therefore, FAST is able to infer student’s knowledge, like Knowledge Tracing does, while also allowing for arbitrary features, like Performance Factor Analysis does. FAST allows general features into Knowledge Tracing by replacing the generative emission probabilities (often called guess and slip probabilities) with logistic regression [3], so that these probabilities can change with time to infer student’s knowledge. FAST allows arbitrary features to train the logistic regression model and the HMM jointly. Training the parameters simultaneously enables FAST to learn from the features. This differs from using regression to analyze the slip and guess probabilities [1]. To validate our approach, we use data collected from real students interacting with a tutor. We present experimental results comparing FAST with Knowledge Tracing and Performance Factor Analysis. We conduct experiments with our model using features like item difficulty, prior successes and failures of a student for the skill (or multiple skills) associated with the item, according to the formulation of Performance Factor Analysis

Impact of Open Social Student Modeling on Self-Assessment of Performance

This study examines the impact of Open Student Modeling (OSM) and its extension known as Open Social Student Modeling (OSSM) on students’ self-assessment of their SQL programming performance. It also explores the relationship between self-assessment and normalized gain. The study was performed with graduate students of University of Pittsburgh taking Database Course for the first semester 2014/2015 and ran for 11 weeks. The results demonstrated that both OSM and OSSM positively affect students’ regular self-assessment ability. However, only OSSM was able to positively impact students’ relative self-assessment ability (i.e., compare their SQL knowledge to others). The results also indicated that, students’ self-assessments are positively and highly correlated to their normalized gain