I-Han Hsiao

Guiding Students to the Right Questions: Adaptive Navigation Support in an E-Learning System for Java Programming

Rapid growth of the volume of interactive questions available to the students of modern E-Learning courses placed the problem of personalized guidance on the agenda of E-Learning researchers. Without proper guidance, students frequently select too simple or too complicated problems and ended either bored or discouraged. This paper explores a specific personalized guidance technology known as adaptive navigation support. We developed JavaGuide, a system, which guides students to appropriate questions in a Java programming course, and investigated the effect of personalized guidance a three-semester long classroom study. The results of this study confirm the educational and motivational effects of adaptive navigation support.

Progressor: social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students.

QuizMap: open social student modeling and adaptive navigation support with TreeMaps

In this paper, we present a novel approach to integrate social adaptive navigation support for self-assessment questions with an open student model using QuizMap, a TreeMap-based interface. By exposing student model in contrast to student peers and the whole class, QuizMap attempts to provide social guidance and increase student performance. The paper explains the nature of the QuizMap approach and its implementation in the context of self-assessment questions for Java programming. It also presents the design of a semester-long classroom study that we ran to evaluate QuizMap and reports the evaluation results.

Open social student modeling: visualizing student models with parallel introspectiveviews

This paper explores a social extension of open student modeling that we call open social student modeling. We present a specific implementation of this approach that uses parallel IntrospectiveViews to visualize models representing student progress with QuizJET parameterized self-assessment questions for Java programming. The interface allows visualizing not only the students own model, but also displaying parallel views on the models of their peers and the cumulative model of the entire class or group. The system was evaluated in a semester-long classroom study. While the use of the system was non-mandatory, the parallel IntrospectiveViews interface caused an increase in all of the usage parameters in comparison to a regular portal-based access, which allowed the student to achieve a higher success rate in answering the questions. The collected data offer some evidence that a combination of traditional personalized guidance with social guidance was more effective than personalized guidance alone.

Adaptive Navigation Support for Parameterized Questions in Object-Oriented Programming

This paper explores the impact of adaptive navigation support on student work with parameterized questions in the domain of object-oriented programming. In the past, we developed QuizJET system, which is able to generate and assess parameterized Java programming questions. More recently, we developed JavaGuide system, which enhances QuizJET questions with adaptive navigation support. This system introduces QuizJET and JavaGuide and reports the results of classroom studies, which explored the impact of these systems and assessed an added value of adaptive navigation support. The results of the studies indicate that adaptive navigation support encourages students use parameterized questions more extensively. Students are also 2.5 times more likely to answer parameterized questions correctly with adaptive navigation support than without such support. In addition, we found that adaptive navigation support especially benefit weaker students helping to close the gap between strong and weak students.

Topic facet modeling: semantic visual analytics for online discussion forums

In this paper, we propose a novel Topic Facet Model (TFM), a probabilistic topic model that assumes all words in single sentence are generated from one topic facet. The model is applied to automatically extract forum posts semantics for uncovering the content latent structures. We further prototype a visual analytics interface to present online discussion forum semantics. We hypothesize that the semantic modeling through analytics on open online discussion forums can help users examine the post content by viewing the summarized topic facets. Our preliminary results demonstrated that TFM can be a promising method to extract topic specificity from conversational and relatively short texts in online programming discussion forums.

Educational social linking in example authoring

Each educational resource management website relies on an authoring tool to provide example content. It takes time and experience for authors to create valuable content. Providing support during authoring can affect the quality and quantity of the examples. In this paper we propose a mashup solution to automatically link community wisdom to authors and ease various difficulties in authoring.

Semantic visual analytics for today's programming courses

We designed and studied an innovative semantic visual learning analytics for orchestrating todays programming classes. The visual analytics integrates sources of learning activities by their content semantics. It automatically processs paper-based exams by associating sets of concepts to the exam questions. Results indicated the automatic concept extraction from exams were promising and could be a potential technological solution to address a real world issue. We also discovered that indexing effectiveness was especially prevalent for complex content by covering more comprehensive semantics. Subjective evaluation revealed that the dynamic concept indexing provided teachers with immediate feedback on producing more balanced exams.

What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling

One of the original goals of intelligent educational systems was to guide each student to the most appropriate educational content. In previous studies, we explored both knowledge-based and social guidance approaches and learned that each has a weak side. In the present work, we have explored the idea of combining social guidance with more traditional knowledge-based guidance systems in hopes of supporting more optimal content navigation. We propose a greedy sequencing approach aimed at maximizing each student’s level of knowledge and implemented it in the context of an open social student modeling interface. We performed a classroom study to examine the impact of this combined guidance approach. The results of our classroom study show that a greedy guidance approach positively affected students’ navigation, increased the speed of learning for strong students, and improved the overall performance of students, both within the system and through end-of-course assessments.

A Study of Mobile Information Exploration with Multi-touch Interactions

Compared to desktop interfaces, touch-enabled mobile devices allow richer user interaction with actions such as drag, pinch-in, pinch-out, and swipe. While these actions have been already used to improve the ranking of search results or lists of recommendations, in this paper we focus on understanding how these actions are used in exploration tasks performed over lists of items not sorted by relevance, such as news or social media posts. We conducted a user study on an exploratory task of academic information, and through behavioral analysis we uncovered patterns of actions that reveal user intention to navigate new information, to relocate interesting items already explored, and to analyze details of specific items. With further analysis we found that dragging direction, speed and position all implied users’ judgment on their interests and they offer important signals to eventually learn user preferences.