Extending Parameterized Problem-Tracing Questions for Java with Personalized Guidance I-Han Hsiao
Sergey Sosnovsky
Peter Brusilovsky
School of Information Sciences, University of Pittsburgh Pittsburgh, PA 15260, USA +1 412 624 9637
School of Information Sciences, University of Pittsburgh Pittsburgh, PA 15260, USA +1 412 624 9637
School of Information Sciences, University of Pittsburgh Pittsburgh, PA 15260, USA +1 412 624 9404
[email protected]
[email protected]
[email protected]
Categories and Subject Descriptors H.5.4 [Hypertext/Hypermedia]: Architectures, Navigation. H.2.3 [Languages]: Programming languages.
General Terms Design, Languages.
Keywords Java, parameterized questions, self-assessment, personalization
ABSTRACT Problem-tracing questions are popular among teachers of various programming languages. In an assessment mode these questions allows to evaluate student knowledge of language semantics. In a self-assessment mode, they provide an excellent learning tool. A 2004 ITiCSE working group report [4] stressed the importance of this type of questions to build foundation of higher-level knowledge. Yet the use of problem-tracing questions is still limited due to a large authoring overhead. To resolve this bottleneck, we explored the idea of parameterized question generation [2]. We developed QuizPACK [1], a system which can generate parameterized problem-tracing questions for C programming language. We also developed QuizGuide [1], a personalized guidance system for QuizPACK, which models student knowledge and guides students individually to most appropriate questions to try. The results of our studies demonstrated that QuizPACK strongly benefits student knowledge and that QuizGuide personalized guidance technology increased student ability to answer questions correctly and encouraged them to use the system more extensively (which, in turn, positively impacted their knowledge) [1]. However, parameterized questions in area of C programming were not as diverse from the complexity point of view as parameterized questions explored in other areas such as physics [2]. As a result, it was left unclear whether personalized guidance technology can successfully guide students to a broader range of questions from relatively simple to very difficult. The work reported in this poster expands our work on parameterized questions to a more sophisticated domain of objectoriented Java programming, which allowed us to introduce questions of much broader. Capitalizing on our experience with QuizPACK, we developed QuizJET (Java Evaluation Toolkit), which supports authoring, delivery, and evaluation of
parameterized questions for Java [3]. We also implemented JavaGuide system (Figure 1), which provides personalized guidance for QuizJET questions. We assessed the impact of adaptive navigation support to student work with questions of different complexity as well as the impact of this technology on weaker and stronger students. The results of two classroom studies indicate that personalized guidance encouraged students to use parameterized questions more extensively and also helped them to access right questions at the right time. Students were 2.5 times more likely to answer a quiz correctly with personalized guidance than without such it. In addition, we found that personalized guidance especially benefited weak students to achieve scores comparable with the scores of strong students on each complexity level of questions.
Figure 1. JavaGuide Interface.
REFERENCES [1] Brusilovsky P, Sosnovsky S: Engaging students to work with self-assessment questions: A study of two approaches, in 10th Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE'2005, pp. 251-255. [2] Kashy, E., Thoennessen, M., Tsai, Y., Davis, N.E., Wolfe, S.L., Using networked tools to enhanse student success rates in large classes, in 27th ASEE/IEEE Frontiers in Education Conference. 1997: Pittsburgh. p. 233-237. [3] Hsiao, I., Brusilovsky, P., Sosnovsky, S. Web-based Parameterized Questions for Object-Oriented Programming. in E-Learn 2008. 2008. Las Vegas, USA: AACE. [4] Lister R, Adams ES, Fitzgerald S, et al.: A multi-national study of reading and tracing skills in novice programmers. ACM SIGCSE bulletin 36:119-150, 2004
