教育背景下的推薦系統越來越重要。大多數研究都以推薦系統的功能性及其有效性為主要研究目標；然而，我們認為推薦系統的設計屬性同樣也為重要之研究議題。因此，本研究藉由一程式評量輔助系統（WPGA），從設計構面探究此系統所提供之推薦功能，是否能有效的傳遞系統所推薦的複習順序以支援學生學習程式設計。我們探討了資訊透明與資訊呈現對於學生感受WPGA的影響，並更深入的探討此系統是否可以幫助學生學習，激勵他們進一步學習並願意推薦其他程式課程來使用WPGA。 本實驗使用解釋說明來控制資訊透明，並使用了兩種呈現方式，包含視覺化與純文字模式。實驗分析方法包含了問卷、眼動儀、與實驗後訪談。研究結果發現，提高資訊透明度在推薦系統中是不可或缺的，基於視覺化的易讀性，學生們更偏好以視覺化的方式來呈現資訊。學生們喜歡此系統所提供的複習順序推薦，並於與推薦系統互動中激勵他們進一步學習的慾望，同時也願意推薦其他程式相關課程使用此系統。眼動儀的結果顯示，學生們的眼動行為在兩種呈現模式中存在顯著差異，學生們在視覺化版本中花的時間比較少。總結來說，系統設計者在設計推薦系統時，應該考慮資訊透明度，且教育者可以利用視覺化的方式來呈現較複雜的知識。

Recommendation system in education context is gaining in importance. While most studies focused on the functionality and effectiveness of the recommendation, we believe the design attribute of the recommendation is as important. The present study proposed a programming learning system, Web Programming Grading Assistant (WPGA), which helps students by recommending a review sequence of questions. To improve the system and recommendation, we investigated the effect of information transparency and information presentation format on the students’ perceptions of the system, and explored if the recommendation could help students learn, motivate them for further learning and being willing to recommend other programming learning courses to apply WPGA. Information transparency was manipulated as explanation provision. Two formats, a visual version and a textual version, were designed to present the explanation. Multiple data analyses including questionnaires, an eye tracker and post experiment interview were utilized. The results showed raising the degree of information transparency is essential in recommendation system, and the students preferred the visual version of explanation as they perceived it easier to interpret. They also liked the recommendation as it motivated them for further learning and they were willing to recommend other programming courses to implement WPGA. Results from the eye tracker showed significant difference as the students spent less time on the visual version. In conclusion, practitioners should consider information transparency while designing recommendation system, and educators can leverage visualization when presenting teaching materials of complex knowledge such as programming.

審定書 i
Acknowledgement ii
摘要 iii
Abstract iv
Table of contents v
1. Introduction 1
1.1 Motivation and background 1
1.2 Research scope 1
1.3 Objective, problem statement, and research questions 3
2. Theoretical Background 4
2.1 programming learning and recommendation systems 4
2.2 Information transparency 6
2.3 Visual and textual presentation 7
2.4 Eye-tracking 9
3. Conceptual model and hypothesis development 11
3. 1 Format and explanation as stimulus 12
3. 2 Understanding of recommendation and perceive learning as organism 13
3.3 Recommend using and learning motivation as responses 15
4. Methodology 17
4.1 Research system 17
4.2 Apparatus 20
4.3 Subjects and experiment procedure 20
4.4 Data analysis 22
5. Result 24
5. 1 Effect of stimuli on understanding of recommendation 24
5. 2 Structural model analyses 28
6. Discussions 28
6.1 The influence of presentation format and explanation 29
6.2 Eye movement and spatial ability 30
6.3 The effect of understanding of recommendation 30
7. Conclusion 31
8. References 32

Abdullah, D., Jayaraman, K., & Kamal, S. B. M. (2016). A Conceptual Model of Interactive Hotel Website: The Role of Perceived Website Interactivity and Customer Perceived Value Toward Website Revisit Intention. Procedia Economics and Finance, 37(16), 170–175.
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49.
Al-busaidi, K. A. (2012). Learners’ perspective on critical factors to LMS success in blended learning : An empirical investigation. Communications of the Association for Information Systems, 30(January 2012), 11–34.
Alenezi, A. R., Karim, A. M. A., & Veloo, A. (2010). An Empirical Investigation into the Role of Enjoyment, Computer Anxiety, Computer Self-efficacy and Internet Experience in Influencing the Students’intention to use E-Learning: a Case Study from Saudi Arabian Governmental Universities. Turkish Online Journal of Educational Technology, 9(4), 22–34.
Awa, veen F., & Krishnan, M. S. (2006). The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization. MIS Quarterly, 30(1), 13–28.
Awad, N. F., & Krishnan, M. S. (2006). The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to Be Profiled Online for Personalization. MIS Quarterly, 13–28.
Barzilai, S., & Blau, I. (2014). Scaffolding game-based learning: Impact on learning achievements, perceived learning, and game experiences. Computers and Education, 70, 65–79.
Belk, R. W. (1975). Situational Variables and Consumer Behavior. Journal of Consumer Research, 2(3), 157.
Benlian, A. (2015). Web personalization cues and their differential effects on user assessments of website value. Journal of Management Information Systems, 32(1), 225–260.
Blanco, C. F., Sarasa, R. G., & Sanclemente, C. O. (2010). Effects of visual and textual information in online product presentations: Looking for the best combination in website design. European Journal of Information Systems.
Buscher, G., & Dumais, S. (2010). The good, the bad, and the random: an eye-tracking study of ad quality in web search. Proceedings of the 33rd International ACM SIGIR, 1–8.
Caspi, A., & Blau, I. (2008). Social presence in online discussion groups: Testing three conceptions and their relations to perceived learning. Social Psychology of Education, 11(3), 323–346.
Changa, H. J., Eckmanb, M., & Yanb, R. N. (2011). Application of the stimulus-organism-response model to the retail environment: The role of hedonic motivation in impulse buying behavior. International Review of Retail, Distribution and Consumer Research, 21(3), 233–249.
Chen, K. C., & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741–752.
Chen, T. W., & Sundar, S. S. (2018). “This app would like to use your current location to better serve you”: Importance of user assent and system transparency in personalized mobile services. Conference on Human Factors in Computing Systems - Proceedings, 2018–April, 1–13.
Chung, J., & Tan, F. B. (2004). Antecedents of perceived playfulness: An exploratory study on user acceptance of general information-searching websites. Information and Management, 41(7), 869–881.
Clark, J., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149–210.
Cook, M. P. (2007). Visual Representations in Science Education: The Influence of Prior Knowledge and Cognitive Load Theory on Instructional Design Principles. Science Education, 91(1), 36–74.
Dascalu, M. I., Bodea, C. N., Moldoveanu, A., Mohora, A., Lytras, M., & De Pablos, P. O. (2015). A recommender agent based on learning styles for better virtual collaborative learning experiences. Computers in Human Behavior, 45, 243–253.
Davis, F. D. (1989). Perceived Usefulness , Perceived Ease of Use , and User Acceptance of. MIS Quarterly, 13(3), 319–340.
Dorça, F. A., Araújo, R. D., de Carvalho, V. C., Resende, D. T., & Cattelan, R. G. (2016). An automatic and dynamic approach for personalized recommendation of learning objects considering students learning styles: An experimental analysis. Informatics in Education, 15(3), 45–62.
Drachsler, H., Hummel, H. G., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. International Journal of Learning Technology, 3(4), 404–423.
Eom, S. B., Wen, H. J., & Ashill, N. (2006). The Determinants of Students’ Perceived Learning Outcomes and Satisfaction in University Online Education: An Empirical Investigation*. Decision Sciences Journal of Innovative Education, 4(2), 215–235.
Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospherics qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177–184.
Éthier, J., Hadaya, P., Talbot, J., & Cadieux, J. (2008). Interface design and emotions experienced on B2C Web sites: Empirical testing of a research model. Computers in Human Behavior, 24(6), 2771–2791.
Farzan, R., & Brusilovsky, P. (2006). Social navigation support in a course recommender system. Proceedings of the 4th International Conference on Adaptive Hypermedia and Apadtive Web-Based Systems, 91–100.
Filieri, R., Alguezaui, S., & McLeay, F. (2015). Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Management, 51, 174–185.
Flavia, C., Sarasa, R. G., & Oru, C. (2010). Effects of visual and textual information in online product presentations : looking for the best combination in website design, (January), 668–686.
Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations-a meta-analytic review. Educational Psychology Review, 22(3), 245–269.
Hong, W., Thong, J. Y. L., & Tam, K. Y. (2004). Designing product listing pages on e-commerce websites: An examination of presentation mode and information format. International Journal of Human Computer Studies, 61(4), 481–503.
Hsiao, I.-H., Huang, P.-K., & Murphy, H. (2017a). Integrating Programming Learning Analytics Across Physical and Digital Space. IEEE Transactions on Emerging Topics in Computing, 6750(c), 1–1.
Hsiao, I.-H., Huang, P.-K., & Murphy, H. (2017b). Uncovering reviewing and reflecting behaviors from paper-based formal assessment. Proceedings of the Seventh International Learning Analytics & Knowledge Conference on - LAK ’17, 319–328.
Hwang, W. Y., Wang, C. Y., Hwang, G. J., Huang, Y. M., & Huang, S. (2008). A web-based programming learning environment to support cognitive development. Interacting with Computers.
Jonassen, D. H., & Grabowski, B. L. (1993). Handbook of individual differences, learning, and instruction. Handbook of individual differences, learning, and instruction.
Jones, A. C. (2008). The Effects of Out-of-Class Support on Student Satisfaction and Motivation to Learn. Communication Education, 57(3), 373–388.
Just, M. A., & Carpenter, P. A. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8(4), 441–480.
Just, M. A., & Carpenter, P. A. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8(4), 441–480.
Keehner, M., Hegarty, M., Cohen, C., Khooshabeh, P., & Montello, D. (2008). Spatial reasoning with external visualizations: What matters is what you see, not whether you interact. Cognitive Science, 32(7), 1099–1132.
Kim, J., & Lennon, S. J. (2013). Effects of reputation and website quality on online consumers’ emotion, perceived risk and purchase intention. Journal of Research in Interactive Marketing, 7(1), 33–56.
Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do Learners Really Know Best? Urban Legends in Education. Educational Psychologist, 48(3), 169–183.
Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers and Education, 56(3), 885–899.
Klein, H., & Noe, R. (2006). Motivation to learn and course outcomes: The impact of delivery mode, learning goal orientation, and perceived barriers and enablers. Personnel Psychology, 59(3), 665–702.
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504.
Koć-Januchta, M., Höffler, T., Thoma, G. B., Prechtl, H., & Leutner, D. (2017). Visualizers versus verbalizers: Effects of cognitive style on learning with texts and pictures – An eye-tracking study. Computers in Human Behavior, 68, 170–179.
Komiak, S. Y. X., & Benbasat, I. (2012). The Effects of Personalization Trust and Adoption of Recommendation. Management Information Systems, 30(4), 941–960.
Lee, H. H., Kim, J., & Fiore, A. M. (2010). Affective and cognitive online shopping experience: Effects of image interactivity technology and experimenting with appearance. Clothing and Textiles Research Journal, 28(2), 140–154.
Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision Support Systems, 52(2), 450–463.
Li, R., Pelz, J., Shi, P., & Haake, A. R. (2012). Learning image-derived eye movement patterns to characterize perceptual expertise. Proceedings of the Annual Meeting of the Cognitive Science Society, 34(34), 1900–1905.
Liang, T., Lai, H., Ku, Y., Liang, T., Lai, H., & Ku, Y. (2017). Personalized Content Recommendation and User Satisfaction : Theoretical Synthesis and Empirical Findings Personalized Content Recommendation and User Satisfaction : Theoretical, 1222(November).
Lorigo, L., Granka, L., Pellacini, F., Pan, B., Haridasan, M., Brynjarsdóttir, H., … Gay, G. (2008). Eye Tracking and Online Search: Lessons Learned and Challenges Ahead. Journal of the American Society for Information Science and Technology, 59(7), 1041–1052.
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32.
Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior, 70, 544–555.
Mahle, M. (2011). Effects of Interactivity on Student Achievement and Motivation in Distance Education. The Quarterly Review of Distance Education, 12(3), 207–215.
Malcolm, G. L., & Henderson, J. M. (2009). The effects of target template speci city on visual search in real-world scenes: Evidence from eye movements. Journal of Vision, 9(2009), 1–13.
Manning, M., Moen, M., & Izenstark, A. (2006). Firm information transparency: Ethical Questions in the Information Age. In IFIP International Conference on Human Choice and Coputers (pp. 145–156).
Mayer, R. E., & Massa, L. J. (2003). Three Facets of Visual and Verbal Learners: Cognitive Ability, Cognitive Style, and Learning Preference. Journal of Educational Psychology, 95(4), 833–846.
Mehigan, T. J., Barry, M., Kehoe, A., & Pitt, I. (2011). Using eye tracking technology to identify visual and verbal learners. In Proceedings - IEEE International Conference on Multimedia and Expo (pp. 1–6).
Newcombe, N. S., & Stieff, M. (2012). Six Myths About Spatial Thinking. International Journal of Science Education, 34(6), 955–971.
Nilashi, M., Jannach, D., Ibrahim, O. bin, Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70–84.
Nodoushan, M. A. S. (2014). Cognitive Versus Learning Styles: Emergence of the Ideal Education Model (IEM). I-Manager’s Journal on Educational Psychology, 8(2), 31.
Ottley, A., Peck, E. M., Harrison, L. T., Afergan, D., Ziemkiewicz, C., Taylor, H. A., … Chang, R. (2015). Improving Bayesian Reasoning The Effects of Phrasing Visualiztion and Spatial. IEEE Transactions on Visualization and Computer Graphics, 22(1).
Peng, C., & Kim, Y. G. (2014). Application of the Stimuli-Organism-Response (S-O-R) Framework to Online Shopping Behavior. Journal of Internet Commerce, 13, 159–176.
Rovai, A. P., & Baker, J. D. (2005). Gender differences in online learning: Sense of community, perceived learning, and interpersonal interactions. The Quarterly Review of Distance Education, 6(1), 31–44.
Singer, J., Elves, R., & Storey, M. A. (2005). NavTracks: Supporting navigation in software maintenance. IEEE International Conference on Software Maintenance, ICSM, 2005, 325–336.
Sinha, R., & Swearingen, K. (2001). Comparing Recommendations Made by Online Systems and Friends. DELOS Workshop on Personalisation and Recommender Systems in Digital Libraries, 106.
Sinha, R., & Swearingen, K. (2002). The role of transparency in recommender systems. In CHI ’02 extended abstracts on Human factors in computing systems - CHI ’02.
Swan, K. (2001). Virtual interaction: Design factors affecting student satisfaction and perceived learning in asynchronous online courses. Education, 22(2), 306–331.
Sweller, J. (1994). Cognitive Load Theory , Learning Difficulty , and Instructional Design. Learning and Instruction, 4, 295–312.
Tsai, C.-H., & Brusilovsky, P. (2017). Providing Control and Transparency in a Social Recommender System for Academic Conferences. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP ’17.
Velez, M. C., Silver, D., & Tremaine, M. (2005). Understanding visualization through spatial ability differences. Proceedings of the IEEE Visualization Conference, 65.
Vermunt, J. D. (1996). Metacognitive, cognitive and affective aspects of learning styles and strategies: A phenomenographic analysis. Higher Education, 31(1), 25–50.
Vesin, B., Ivanović, M., Klašnja-Milićević, A., & Budimac, Z. (2012). Protus 2.0: Ontology-based semantic recommendation in programming tutoring system. Expert Systems with Applications, 39(15), 12229–12246.
Wang, Q., Yang, S., Liu, M., Cao, Z., & Ma, Q. (2014). An eye-tracking study of website complexity from cognitive load perspective. Decision Support Systems, 62, 1–10.
Wang, Y. J., Hernandez, M. D., & Minor, M. S. (2010). Web aesthetics effects on perceived online service quality and satisfaction in an e-tail environment: The moderating role of purchase task. Journal of Business Research, 63(9–10), 935–942.
Wu, D., Hiltz, S. R., & Bieber, M. (2010). Acceptance of Educational Technology : Field Studies of Asynchronous Participatory Examinations. Technology, 26(21), 451–476.
Wu, W. Y., Lee, C. L., Fu, C. su, & Wang, H. C. (2013). How can online store layout design and atmosphere influence consumer shopping intention on a website? International Journal of Retail & Distribution Management, 42(1), 4–24.
Xiao, B., & Benbasat, I. (2007). E-Commerce Product Recommendation Agents Use, Characteristics, and Impact. MIS Quarterly, 31(1), 137–209.
Xu, J. D., Benbasat, I., & Cenfetelli, R. T. (2014). The Nature And Consequences OF Trade-off Transparency In The Context Of Recommendation Agents. MIS Quarterly, 38(2), 379–406.
Zimmermann, T., Weißgerber, P., Diehl, S., & Zeller, A. (2005). Mining version histories to guide software changes. IEEE Transactions on Software Engineering, 31(6), 429–445.