Evaluation of topic-based adaptation and student modeling in QuizGuide

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

Guiding and motivating students through open social student modeling: Lessons learned

Background/Context: A large number of educational resources are now made available on the web to support both regular classroom learning and online learning. The abundance of available content has produced at least two problems: how to help students find the most appropriate resources and how to engage them in using and benefiting from these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work attempts to integrate these directions of research by combining the ideas of adaptive navigation support and open student modeling with the ideas of social comparison and social visualization. We call our approach Open Social Student Modeling (OSSM). Objective/Research Questions: First, we review a sequence of our earlier projects focused on Open Social Student Modeling for one kind of learning content and formulate several key design principles that contribute to the success of OSSM. Second, we present our exploration of OSSM in a more challenging context of modeling student progress for two kinds of learning content in parallel. We aim to answer the following research questions: How do we design OSSM interfaces to support many kinds of learning content in parallel? Will current identified design principles (key features) confirm the power of the learning community through OSSM with multiple learning-resource collections? Will the OSSM visualization provide successful personalized guidance within a richer collection of educational resources? Research Design: We designed four classroom studies to assess the value of different options for OSSM visualization of one and multiple kinds of learning content in the context of programming-language learning. We examined the comparative success of different design options to distill successful design patterns and other important lessons for the future developers of OSSM for personalized and social e-learning. Findings/Results: The results confirmed the motivational impact of personalized social guidance provided by the OSSM system in the target context. The interface encouraged students to explore more topics and motivated them to work ahead of the course schedule. Both strong and weak students worked with the appropriate levels of questions for their readiness, which yielded consistent performance across different levels of complex problems. Additionally, providing more realistic content collection on the navigation-supported OSSM visualizations resulted in uniform performance for the group. Conclusions/Recommendation: A sequence of studies of several OSSM interfaces confirmed that a combination of adaptive navigational support, open student modeling, and social visualization in the form of the OSSM interface can reinforce the navigational and motivational values of these approaches. In several contexts, the OSSM interface demonstrated its ability to offer effective guidance in helping students to locate the most relevant content at the right time while increasing student motivation to work with diverse learning content

Adaptive hypertext and hypermedia : workshop : proceedings, 3rd, Sonthofen, Germany, July 14, 2001 and Aarhus, Denmark, August 15, 2001

This paper presents two empirical usability studies based on techniques from Human-Computer Interaction (HeI) and software engineering, which were used to elicit requirements for the design of a hypertext generation system. Here we will discuss the findings of these studies, which were used to motivate the choice of adaptivity techniques. The results showed dependencies between different ways to adapt the explanation content and the document length and formatting. Therefore, the system's architecture had to be modified to cope with this requirement. In addition, the system had to be made adaptable, in addition to being adaptive, in order to satisfy the elicited users' preferences

Adaptive hypertext and hypermedia : workshop : proceedings, 3rd, Sonthofen, Germany, July 14, 2001 and Aarhus, Denmark, August 15, 2001

This paper presents two empirical usability studies based on techniques from Human-Computer Interaction (HeI) and software engineering, which were used to elicit requirements for the design of a hypertext generation system. Here we will discuss the findings of these studies, which were used to motivate the choice of adaptivity techniques. The results showed dependencies between different ways to adapt the explanation content and the document length and formatting. Therefore, the system's architecture had to be modified to cope with this requirement. In addition, the system had to be made adaptable, in addition to being adaptive, in order to satisfy the elicited users' preferences

NAVIGATION SUPPORT AND SOCIAL VISUALIZATION FOR PERSONALIZED E-LEARNING

A large number of educational resources is now made available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produced at least two problems: how to help students to find the most appropriate resources and how to engage them into using these resources and benefit from them. Personalized and social learning have been suggested as potential ways to address these problems. This work attempts to combine the ideas of personalized and social learning by providing navigation support through an open social student modeling visualization. A series of classroom studies exploited the idea of the approach and revealed promising results, which demonstrated the personalized guidance and social visualization combined helped students to find the most relevant resources of parameterized self-assessment questions for Java programming. Thus, this dissertation extend the approach to a larger collection of learning objects for cross content navigation and verify its capability of supporting social visualization for personalized E-Learning. The study results confirm that working with the non-mandatory system, students enhanced the learning quality in increasing their motivation and engagement. They successfully achieved better learning results. Meanwhile, incorporating a mixed collection of content in the open social student modeling visualizations effectively led the students to work at the right level of questions. Both strong and weak student worked with the appropriate levels of questions for their readiness accordingly and yielded a consistent performance across all three levels of complexities. Additionally, providing a more realistic content collection on the navigation supported open social student modeling visualizations results in a uniform performance in the group. The classroom study revealed a clear pattern of social guidance, where the stronger students left the traces for weaker ones to follow. The subjective evaluation confirms the design of the interface in terms of the content organization. Students’ positive responses also compliment the objective system usage data

Providing Service-based Personalization in an Adaptive Hypermedia System

Adaptive hypermedia is one of the most popular approaches of personalized information access. When the field started to emerge, the expectation was that soon nearly all published hypermedia content could be adapted to the needs, preferences, and abilities of its users. However, after a decade and a half, the gap between the amount of total hypermedia content available and the amount of content available in a personalized way is still quite large.In this work we are proposing a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content.To empirically prove the viability of our approach, we developed PERSEUS - server of adaptation functionalities. First, we confirmed that the conceptual design of PERSEUS supports realization of a several of the widely used adaptive hypermedia techniques. Second, to demonstrate that the extracted adaptation does not create a significant computational bottleneck, we conducted a series of performance tests. The results show that PERSEUS is capable of providing a basis for implementing computationally challenging adaptation procedures and compares well with alternative, not-encapsulated adaptation solutions. As a result, even on modest hardware, large user populations can be served content adapted by PERSEUS