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A Framework for SQL Learning: Linking Learning Taxonomy, Cognitive Model and Cross Cutting Factors

Authors: Huda Al Shuaily, Karen Renaud


Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords: Pattern, SQL, learning, model.

Digital Object Identifier (DOI):

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[1] Aamodt, A. (1991). A knowledge-intensive, integrated approach to problem solving and sustained learning. Knowledge Engineering and Image Processing Group. University of Trondheim, 27-85.
[2] Aiken Jr, L. R. (1976). Update on attitudes and other affective variables in learning mathematics. Review of Educational Research, 293-311.
[3] Al-Shuaily, H. (2012, July 2012). Analyzing the Influence of SQL Teaching and Learning Methods and Approaches. Paper presented at the 10th International Workshop on the Teaching, Learning and Assessment of Databases. UK, London
[4] Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students' learning strategies and motivation processes. Journal of Educational Psychology, 80(3), 260.
[5] Anderson, J. (1987). Skill acquisition: Compilation of weak-method problem solutions. Psychological Review, 92, 192-210.
[6] Anderson, L. W. E., Krathwohl, D. R. E., Airasian, P. W., K.A.Cruikshank, R.E.Mayer, P.R.Pintrich, . . . M.C.Wittrock. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives (Complete edition). New York: Longman.
[7] Bloom, B. S., & Broder, L. J. (1950). Problem-solving Processes of College Students: An Explanatory Investigation: University of Chicago Press.
[8] Bonar, J., & Soloway, E. (1985). Preprogramming Knowledge: A major source of misconceptions in novice programmers. Human-Computer Interaction, 1, 133-161.
[9] Caraway, S. D. (1985). Factors Influencing Competency in Mathematics Among Entering Elementary Education Majors.
[10] Cutts, Q., Esper, S., Fecho, M., Foster, S. R., & Simon, B. (2012). The abstraction transition taxonomy: developing desired learning outcomes through the lens of situated cognition 10.1145/2361276.2361290 Proceedings of the ninth annual international conference on International computing education research (pp. 63-70). Auckland, New Zealand: ACM.
[11] Davies, N., & Savell, J. (2000). Maths is like a bag of tomatoes": Student attitudes upon entry to an Early Years teaching degree. Paper presented at the Teacher Education Forum of Aotearoa New Zealand Conference, Christchurch.
[12] Felix, S. (1981). Competing cognitive structures in second language acquisition. Paper presented at the European-North American Workshop on Cross-Linguistic Second Language Acquisition Research, Lake Arrowhead CA, September. Cited and discussed in Birgit Harley (1986) Age in Second Language Acquisition. Clevedon: Multilingual Matters.
[13] Fennema, E., & Sherman, J. (1977). Sex-related differences in mathematics achievement, spatial visualization and affective factors. American educational research journal, 14(1), 51-71.
[14] Fuller, U., Johnson, C. G., Ahoniemi, T., Cukierman, D., Hern\, I., \#225, . . . Thompson, E. (2007). Developing a computer science-specific learning taxonomy. SIGCSE Bull., 39(4), 152-170. doi: 10.1145/1345375.1345438
[15] Gorman, M. E. (2002). Types of Knowledge and Their Roles in Technology Transfer The Journal of Technology Transfer (Vol. 27, pp. 219-231): Springer Netherlands.
[16] Gray, P. H. (2001). A problem-solving perspective on knowledge management practices. Decision Support Systems, 31(1), 87-102. Grootenboer, P. (2010). Beliefs, attitudes and feelings students learn about mathematics. Far East Journal of Mathematical Education, 5(1), 31-52.
[17] Ismail, M. N., Azilah, N., Naufal, U., & Kelantan, U. T. M. C. (2010). Instructional strategy in the teaching of computer programming: A need assessment analyses. TOJET, 9(2), 125-131.
[18] Johnson, C. G., & Fuller, U. (2006). Is Bloom's taxonomy appropriate for computer science? Paper presented at the Proceedings of the 6th Baltic Sea conference on Computing education research: Koli Calling 2006.
[19] Lahtinen, E. (2007). A categorization of novice programmers: A cluster analysis study. Paper presented at the Proceedings of the 19th annual Workshop of the Psychology of Programming Interest Group.
[20] Laio, C., & Palvia, P. C. (2000). The impact of data models and task complexity on end-user performance: an experimental investigation. doi: 10.1006/ijhc.1999.0358. International Journal of Human-Computer Studies, 52(5), 831-845.
[21] Linn, M. C., & Clancy, M. J. (1992). The case for case studies of programming problems. Communications of the ACM, 35(3), 121-132.
[22] Ma, X., & Kishor, N. (1997). Assessing the relationship between attitude toward mathematics and achievement in mathematics: A meta-analysis. Journal for research in mathematics education, 26-47.
[23] Mannino, M. V. (2001). Database Application Development and Design, : McGraw-Hill Company,Inc.
[24] Mayer, R. E. (2008). Learning and instruction: Merrill.
[25] McGill, T. J., & Volet, S. E. (1997). A conceptual framework for analysing students' knowledge of programming. Journal of Research on Computing in Education, 29(3), 276-297.
[26] Merrill, M. D. (2000, 2000). Knowledge objects and mental models. Paper presented at the Advanced Learning Technologies, 2000. IWALT 2000. Proceedings. International Workshop on.
[27] Mohtashami, M., & Scher, J. M. (2000). Application of Bloom's Cognitive Domain Taxonomy to Database Design. Paper presented at the Proceedings of ISECON (information systems educators conference).
[28] Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. New Perspectives on Affect and Learning Technologies, 23-39.
[29] Reisner, P. (1977). Use of Psychological Experimentation as an Aid to Development of a Query Language. Software Engineering, IEEE Transactions on, SE-3(3), 218-229.
[30] Reisner, P. (1981). Human Factors Studies of Database Query Languages: A Survey and Assessment. ACM Comput. Surv., 13(1), 13-31.
[31] Reisner, P., Boyce, R. F., & Chamberlin, D. D. (1975). Human factors evaluation of two data base query languages: square and sequel Proceedings of the May 19-22, 1975, national computer conference and exposition (pp. 447-452). Anaheim, California: ACM.
[32] Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137-172.
[33] Rodrigo, M. M. T., Baker, R. S., Jadud, M. C., Amarra, A. C. M., Dy, T., Espejo-Lahoz, M. B. V., . . . Tabanao, E. S. (2009). Affective and behavioral predictors of novice programmer achievement. Paper presented at the ACM SIGCSE Bulletin.
[34] Schlager, M. S., & Ogden, W. C. (1986a). A cognitive model of database querying: a tool for novice instruction. SIGCHI Bull., 17(4), 107-113.
[35] Schlager, M. S., & Ogden, W. C. (1986b). A cognitive model of database querying: a tool for novice instruction 10.1145/22339.22357. SIGCHI Bull., 17(4), 107-113.
[36] Shneiderman, B. (1978). Improving the human factors aspect of database interactions. ACM Trans. Database Syst., 3(4), 417-439.
[37] Shneiderman, B., & Mayer, R. (1979). Syntactic/semantic interactions in programmer behavior: A model and experimental results. International Journal of Parallel Programming, 8(3), 219-238.
[38] Soloway, E., & Spohrer, J. C. (1989). Studying the novice programmer: Lawrence Erlbaum Hillsdale, NJ.
[39] Starr, C. W., Manaris, B., & Stalvey, R. A. H. (2008). Bloom's taxonomy revisited: specifying assessable learning objectives in computer science. Paper presented at the ACM SIGCSE Bulletin.
[40] Suh, K. S., & Perkins, W. C. (1994, 4-7 Jan. 1994). The effects of a system echo in a restricted natural language database interface for novice users. Paper presented at the System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on
[41] Thomas, J. C., & Gould, J. D. (1975). A psychological study of query by example Proceedings of the May 19-22, 1975, national computer conference and exposition (pp. 439-445). Anaheim, California: ACM.
[42] Vassiliou, Y., Jarke, M., Stohr, E. A., Turner, J. A., & White, N. H. (1983). Natural Language for Database Queries: A Laboratory Study. MIS Quarterly, 7(4), 47-61.
[43] Welty, C., & Stemple, D. W. (1981). Human factors comparison of a procedural and a nonprocedural query language. ACM Trans. Database Syst., 6(4), 626-649.
[44] Winograd, P., & Hare, V. (1988). Direct instruction of reading comprehension strategies: The nature of teacher explanation. San Diego: Academic Press.