Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge

Students learn new instructions well by building on relevant prior knowledge, as it affects how instructors and students interact with the learning materials. Moreover, studies have found that good prior knowledge can enable students to attain better learning motivation, comprehension, and performance. This suggests it is important to assist students in obtaining the relevant prior knowledge, as this can enable them to engage meaningfully with the learning materials. Tests are often used to help instructors assess students' prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to each student, and this may mean that students are unable to realize their own individual weaknesses. To address this problem, instructors can diagnose the test results to provide more detailed information to each student, but this is obviously a time-consuming process. Therefore, this study proposes a testing-based diagnosis system to assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken. Furthermore, an experiment was conducted to evaluate the effectiveness of the proposed approach in an interdisciplinary course, since several studies have indicated that students learn more and better in such courses when applying relevant prior knowledge to what they are learning. The experimental results show that the developed system is able to effectively diagnose students' prior knowledge and enhance their learning motivation and performance on an interdisciplinary course. In addition, two diagnostic evaluations were also conducted to assess whether the diagnoses given by the system were consistent with the decisions of experts. The results demonstrate that the proposed system can effectively assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken, since the diagnoses produced by the system were broadly consistent with those of experts.

[1]  Gudela Grote,et al.  Distributed collaboration activities in a blended learning scenario and the effects on learning performance , 2007, J. Comput. Assist. Learn..

[2]  F. Dinter,et al.  Instruction and Mental Model Progression: Learner‐Dependent Effects of Teaching Strategies on Knowledge Acquisition and Analogical Transfer , 1995 .

[3]  Daniel C. Moos,et al.  Self-regulated learning with hypermedia : The role of prior domain knowledge , 2008 .

[4]  D. Treagust.  Development and use of diagnostic tests to evaluate students’ misconceptions in science , 1988 .

[5]  Gwo-Jen Hwang,et al.  A test-sheet-generating algorithm for multiple assessment requirements , 2003, IEEE Trans. Educ..

[6]  Daniel A. Levinthal,et al.  ABSORPTIVE CAPACITY: A NEW PERSPECTIVE ON LEARNING AND INNOVATION , 1990 .

[7]  Bhekuzulu Khumalo.  The Fundamental Theory of Knowledge , 2006 .

[8]  Li Hongzheng,et al.  Advice to Mental Health Intervention for Recruits Based on an Investigation for Mental Status of Servicemen during Basic Military Training , 2007 .

[9]  John Sweller,et al.  The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications , 2005 .

[10]  Ton de Jong,et al.  Scientific Discovery Learning with Computer Simulations of Conceptual Domains , 1998 .

[11]  Jay F. Nunamaker,et al.  Can e-learning replace classroom learning? , 2004, CACM.

[12]  Jia-Sheng Heh,et al.  Learning and diagnosis of individual and class conceptual perspectives: an intelligent systems approach using clustering techniques , 2005, Comput. Educ..

[13]  Gwo-Jen Hwang,et al.  A conceptual map model for developing intelligent tutoring systems , 2003, Comput. Educ..

[14]  Danielle S. McNamara,et al.  Prior knowledge, reading skill, and text cohesion in the comprehension of science texts , 2009 .

[15]  P. Pintrich,et al.  Motivational and self-regulated learning components of classroom academic performance. , 1990 .

[16]  Gwo-Jen Hwang,et al.  An auto-scoring mechanism for evaluating problem-solving ability in a web-based learning environment , 2009, Comput. Educ..

[17]  Siu Cheung Kong,et al.  A study of building a resource-based learning environment with the inquiry learning approach: Knowledge of family trees , 2008, Comput. Educ..

[18]  L. Ivanitskaya,et al.  Interdisciplinary Learning: Process and Outcomes , 2002 .

[19]  Robert D. Macredie,et al.  Hypermedia learning and prior knowledge: domain expertise vs. system expertise , 2005, J. Comput. Assist. Learn..

[20]  Filip Dochy,et al.  Integrating assessment, learning and instruction: Assessment of domain-specific and domaintranscending prior knowledge and progress , 1996 .

[21]  N. B. Biswas.  Knowledge and pedagogy: An essential proposition in response to teacher preparation , 2007 .

[22]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[23]  Gwo-Jen Hwang,et al.  A multi-expert approach for developing testing and diagnostic systems based on the concept-effect model , 2010, Comput. Educ..

[24]  Yuehui Chen,et al.  Computational Intelligence in Bioinformatics , 2008, Computational Intelligence in Bioinformatics.

[25]  C.-C. Wu,et al.  Using handhelds in a Jigsaw cooperative learning environment , 2006, J. Comput. Assist. Learn..

[26]  Gwo-Jen Hwang,et al.  An innovative parallel test sheet composition approach to meet multiple assessment criteria for national tests , 2008, Comput. Educ..

[27]  Yen-Ting Lin,et al.  Automatic Leveling System for E-Learning Examination Pool Using Entropy-Based Decision Tree , 2005, ICWL.

[28]  Yen-Ting Lin,et al.  Effectiveness of a Mobile Plant Learning System in a science curriculum in Taiwanese elementary education , 2010, Comput. Educ..

[29]  Edward Keedwell,et al.  Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems , 2005 .

[30]  Teresa Martín-Blas,et al.  Evaluating background and prior knowledge: A case study on engineering graphics learning , 2009, Comput. Educ..

[31]  Rodney L. Doran,et al.  Basic measurement and evaluation of science instruction , 1980 .

[32]  Yen-Ting Lin,et al.  An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization , 2010, Comput. Educ..

[33]  R. Moreno.  Decreasing Cognitive Load for Novice Students: Effects of Explanatory versus Corrective Feedback in Discovery-Based Multimedia , 2004 .

[34]  Yueh-Min Huang,et al.  A blog-based dynamic learning map , 2008, Comput. Educ..

[35]  William C. Chu,et al.  ANTS: Agent-Based Navigational Training System , 2005, ICWL.

[36]  Yen-Ting Lin,et al.  An adaptive testing system for supporting versatile educational assessment , 2009, Comput. Educ..

[37]  Yen-Ting Lin,et al.  Dynamic question generation system for web-based testing using particle swarm optimization , 2009, Expert Syst. Appl..

[38]  Charlene M. Czerniak,et al.  A Literature Review of Science and Mathematics Integration , 1999 .

[39]  Meng Chang Chen,et al.  Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community , 2010, Comput. Educ..