Academic journal article Journal of Information Systems Education

Examining Learning Styles and Perceived Benefits of Analogical Problem Construction on SQL Knowledge Acquisition

Academic journal article Journal of Information Systems Education

Examining Learning Styles and Perceived Benefits of Analogical Problem Construction on SQL Knowledge Acquisition

Article excerpt


Recent studies have highlighted the importance of improving education for a new generation of students in data-driven information systems (IS) educational areas (Chen, Chiang, and Storey, 2012; Chiang, Goes, and Stohr, 2012). Among courses offered by IS faculty, database management is the most consistently offered course among the IS 2010 curriculum model (IS 2010.2) with approximately 97% of programs including this class (Bell, Mills, and Fadel, 2013). Demand for graduates with expertise in Structured Query Language (SQL) and database management remains strong. The growth of SQL is often attributed to its role as a standard data access method for big data (Soat, 2014). Both Computer Weekly and ZDNet find SQL as the software skill most in demand (Flinders, 2011; Lomas, 2011). Student's graduating with a background in database often find themselves in a unique situation when interviewing for a professional IS position. Many interviews not only require students to be proficient in database nomenclature, but are often required to write structured query language code to solve business problems as part of the interview (Kadlec, 2008).

Many professors have recognized the importance of writing code by organizing coding labs for applied practice. Unfortunately, database education often includes lectures and slides with lots of terminology as the only instructional approach to transfer this knowledge to the learner. As a result, students are unprepared to solve complex problems in industry including the rigorous interviewing process (Schank, 2002; Tang, Lee, and Koh, 2000).

Strategically bridging new information to an individual's prior knowledge is documented in the literature as a way to improve the learning process (Catrambone and Holyoak, 1989; Gentner and Holyoak, 1997). Analogy problem construction represents an instructional strategy used to link prior knowledge to new knowledge (Togo, 2002). Analogy problem construction involves students creating their own analogous problems to better understand and retain new knowledge (Bernardo, 2001). The focus of this technique is to tap into a learner's existing knowledge structures and leverage this prior knowledge to new knowledge. The use of analogies in learning are credited as among the most effective method of solidifying abstract concepts to better understanding and retention of new knowledge (Dincer, 2011). As a result, linking new knowledge to personal knowledge contribute to meaningful, active, and effective learning (Seyihoglu and Ozgurbuz, 2015).

Prior research also suggests learning styles potentially have an impact on user learning (Bostrom, Olfman, and Sein, 1990). The Felder and Soloman Index of Learning Styles instrument was specifically created to identify learning styles in a classroom setting (De Vita, 2001). The instrument organizes competing learning styles, which include active versus reflective, sensing versus intuitive, visual versus verbal, and sequential versus global preferences. Felder (1993) argues sound instruction should incorporate a variety of teaching styles addressing each side of the learning dimensions at least part of the time.

Although research related to analogy problem construction and learning styles are examined in some detail (Cellucci et al., 2011; Togo, 2002; Zheng et al., 2008), there is a dearth of research in information systems education (Cegielski, Hazen, and Rainer, 2011). Felder and Silverman (1988) argue students absorb concepts more quickly when instructional strategies are consistent with the student's learning style. A recent information systems study supports the value of matching activities with learning styles when possible. The study examined learning styles and object-oriented computer programming and found performance increases when the instructional strategies closely matched the student's learning style (Cegielski, Hazen, and Rainer, 2011). The authors conclude the research "serve[s] as a foundation from which to launch a detailed research agenda in the area of learning styles within the IS educational domain" (Cegielski, Hazen, and Rainer, 2011, p. …

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