The number of new graduates entering the Information and Communications Technology (ICT) industry is becoming a critical issue in our economy. At present, this number is insufficient to meet industry requirements (Information and Communications Technology Council, 2008). This shortage of graduates can be attributed to a number of factors, one of which is the ability of schools of computer science and information technology to attract and retain new students. Often, students enrolling in these programs drop out due to academic reasons. Therefore a need exists to identify those factors which correlate with academic performance among ICT graduates. These factors could then be used to “pre-screen” those students interested in a career in information technology. Such a prescreening would help students self-reflect on whether a career in ICT is suitable for them before committing the financial and emotional resources and time in undertaking a course of study for which they may not be suited. At the same time, such a tool could help other students who had not considered an ICT career to quickly determine whether they share qualities typical of ICT graduates.
The Information and Communications Technology Council (ICTC), the federal industry labour force advisory panel for ICT, predicts skills shortages across the country and actual labour shortages in Western Canada through 2015. In 2007, the economy added approximately 16,800 ICT jobs—ahead of the forecasted 14,500. In the first half of 2008, a further 4,200 jobs were added. In its 2008 forecast, the ICTC predicts net job growth plus replacement for retiring workers will require 15,795 to 22,345 jobs per year (Information and Communications Technology Council, 2008). Yet, across Canada, enrollment in post-secondary computer science programs is in a prolonged decline with enrollment numbers down 33% from their 2001-2002 peak (Information and Communications Technology Council, 2008). Within Atlantic Canada, enrollments are down 37% from their 2001-2002 levels. Ontario and Quebec are between 50-60% of their 2001-2002 levels. Enrollment across the Prairies is at 65% of its peak. Only British Columbia has maintained enrollment in Computer Science (CS) program, maintaining 97% of its 2001-2002 level. Baccalaureate graduation numbers across the country have declined from 4,900 in 2003-2004 to 3,300 in 2006-2007 and have continued to decline (Slonim, Scully, & McAlliser, 2008).
Significant issues are emerging about how to successfully teach computer programming at the pos-secondary level in North America. Slonim, Scully, and McAllister (2008) observed that Computer Science (CS) programs in North America typically have a low retention rate from first to second year. Instructors teaching in the disciplines of Information Technology and Computer Science (IT/CS) frequently lament that some students just can’t seem to grasp the necessary skills to be successful in their studies. David LeBlanc, Chair of the Computer Science Department at UPEI states that only 12-25% of students entering his program will graduate; Holland College, also in PEI, expects to lose 50% of its information technology students between the first and second years (Canadian Broadcasting Corporation, 2010). This statistic is also validated by the retention rate within the Computer Information Technology program at Lethbridge College. Often IT students seem to have an inability to understand the logic, process and methodology behind algorithmic development—a foundational topic in the field of information technology (Thomas, Ratcliffe, Woodburry, & Jarman, 2002).
Given the time and cost of educating a post-secondary student, the current state of affairs seems like an inefficient use of resources by both the student and the institution. Therefore, is it possible to identify those factors which correlate with student success in computer science and information technology programs? And if so, what are these factors? In attempting to isolate factors that can predict success in IT/CS studies, a survey of available literature points to four areas that show promising results:
The question of how to operationally define motivation is not trivial. Le, Casillas, Robbins and Langley (2005) developed the Student Readiness Inventory or SRI to provide metrics on the personality dimensions most relevant to post-secondary academic success. They found that conscientiousness, goal-focus, and academic self-confidence are the strongest components of academic motivation (Le, Casillas, Robbins, & Langley, 2005). Using another measure, the Big Five Inventory or BFI, Komarraju and Karau (2005) found that the BFI’s conscientiousness scale strongly correlated with academic motivation. In independent confirmation of this finding, Peterson, Casillas and Robbins (2006) found strong correlation between the Conscientious scale of the Big Five Inventory (BFI) and motivation as measured by the Student Readiness Inventory (SRI).
It a study of almost 4,000 students from 28 different institutions offering both two- and four-year programs, and including subjects from a wide variety of disciplines, Allen and Robbins (2010) tried to correlate “interest-major” congruence, motivation as measured by the SRI, and first-year performance as predictors of students’ completion of their studies within each program’s nominal duration, which they use as their operational definition of achievement. They found that only 12% of students in a two-year program completed their studies in the nominal period. Among students taking a two-year program, only motivation was significant and even then, only indirectly. While motivation did not directly correlate to achievement, it did correlate with first-year performance and first-year performance did correlate with achievement (Allen & Robbins, 2010).
In another study of why students underachieve, Balduf (2009) looked at individual study skills. She reported that nearly all students—even those with strong high school grades—reported that “high school did not require them to work hard enough and felt that they earned high grades without expending much effort” (2009, p. 284). Her research found that many students enter post-secondary programs without effective study skills or adequate time management abilities and that students were externally motivated (eg. looking for grades or parental rewards) rather than being internally motivated (eg. seeking understanding, understanding, or integration of various knowledge domains). She reports that students also found that the amount of freedom in post-secondary institutions was an additional challenge to their time management skills.
A more robust study of time management and student success used a combination of GPA results and a set of questionnaires administered to both the participants and a friend of each participant. This study found that time management skills, intelligence, time spent studying, waking up earlier, owning a computer, spending less time in passive leisure, and eating a healthy diet were all significant predictors of positive GPA results (George, Dixon, Stansal, Gelb, & Pheri, 2008).
Studies have also been conducted to determine if there is any relationship between students’ learning style and their success in learning particular programming languages (Thomas, Ratcliffe, Woodburry, & Jarman, 2002). Similar research on how students can best learn computer programming languages have focused on what are the best teaching styles to complement the various learning styles of students (Bayman & Mayer, 1988). Learning styles have been measured using the Myers-Briggs Type Indicator (MBTI), the Index of Learning Styles (ILS) Questionnaire (Soloman & Felder, 2009), and the Gregorc Style Delineator (GSD) (Gregorc, 1984).
Using the ILS, Thomas et al. (2002) found that reflective learners were better programmers than active learners, and verbal learners had higher scores than visual learners (2002). Davidson and Saveyne (1992), using the GSD found that abstract sequential mindsets correlate with a better ability to learn a programming language than other mindsets while those with abstract random mindsets have a negative correlation. There was no correlation among those students with a concrete learning style.
Another avenue of inquiry has recently started to look at the specific problems encountered by students in IT/CS programs. Students in these courses often encounter a level of abstraction in their subject matter unfamiliar to students in many other disciplines. Forming algorithms to solve computational problems often involves a number of different types of logical processing within the same problem space. Definitions in this knowledge domain often involve negation with conditional conjunction or disjunction. An example could be the definition of a “ball” in North American baseball: “A pitch at which the batter does not swing and which does not pass through the strike zone” (Goodwin & Johnson-Laird, 2010). For those familiar with the game, this definition seems self-evident, but for a person learning new skills (such as programming), the logic to conceptualize such an abstract concept can be daunting. Similarly, the definition that “x owns y” means that (in part), “it is permissible for x to use y, and not permissible for others to prevent x from using y” (Goodwin & Johnson-Laird, 2010).
Goodwin and Johnson-Laird hypothesize that people form mental models of these Boolean expressions that are not often correct in their logical representations. They found that people generally have a difficult time considering all the possibilities of such Boolean paradigms.
Stanovich (2002) argues that cognitive capacity (such as the ability to do well on an IQ test) is distinct from a person’s thinking disposition—a term that he uses to refer to the intersection of cognitive ability or intelligence, and personality traits such as will, desire and behaviour. Stanovich (2002) then suggests that “thinking dispositions can predict performance on reasoning and rational thinking tasks even after individual differences in measures of general cognitive ability have been partialled [sic] out” (p. 131). One type of thinking disposition, which Toplak and Stanovich (2002) call disjunctive reasoning or “as the tendency to consider all possible states of the world when deciding among options or when choosing a problem solution in a reasoning task” (p. 197), may be significant in identifying those people who can process complex logical problems. In a study involving nine disjunctive logic problems, a number of the problems tested generalized disjunctive thinking dispositions and these test items show a stronger association with thinking styles (or dispositions) than with cognitive ability and capacity, thus strengthening the argument in favour of a differentiation between the two constructs (Toplak & Stanovich, 2002).
A sub-type of complex reasoning problems known as “Analytical Reasoning” problems, have until recently been part of the Graduate Record Examination (GRE), an entrance test widely used for candidates to graduate schools throughout Europe and North America. They remain part of the Law School Admission Test (LSAT) and discussions are on-going about resurrecting their use in the GRE (Newstead, Bradon, Handley, Dennis, & Evans, 2006). In this class of problem, a narrative of the scenario contains the initial context and a list of the rules allowed in the problem space. The subject is then presented with a list of questions or problems. Successfully arriving at the solution involves a deep semantic understanding of the scenario and the ability to parse out the embedded rules. Only then can the subject start to solve the problem. Problem solving involves identifying which rule to apply first in order to reduce the number of possible options in the solution set as quickly as possible.
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