A Motivation Guided Holistic Rehabilitation of the First Programming Course

Abstract

It has been estimated that more than two million students started computing studies in 1999 and 650,000 of them either dropped or failed their first programming course. For the individual student, dropping such a course can distract from the completion of later courses in a computing curriculum and may even result in changing their course of study to a curriculum without programming. In this article, we report on how we set out to rehabilitate a troubled first programming course, one for which the dropout statistic and repercussion was evident. The five-year longitudinal case study described in this article began by systematically tracking the pass rate of a first programming course, its throughput, as proposed by the Theory of Constraints. The analyses of these data indicated three main problems in the course: programming discipline difficulty, course arrangement complexity, and limited student motivation. The motivation problem was approached from the Two-Factor Theory point of view. It investigated those factors that led to dissatisfaction among the students, the hygiene factors, and those factors that led to satisfaction, the intrinsic and extrinsic motivators. The course arrangement complexity was found to be a hygiene factor, while the lack of extrinsic and intrinsic motivators contributed to the high dropout rates. The course improvement efforts made no attempt to change the inherent characteristics of the programming discipline, but introduced holistic changes in the course arrangements over a five-year period, from 2005 to 2009, to eliminate the hygiene factors and to increase motivational aspects of the course. This systems approach to course improvement resulted in an increase in the pass rate, from 44% prior to the changes to 68% thereafter, and the overall course atmosphere turned positive. This paper reports on the detailed changes that were made and the improvements that were achieved over this five-year period.

Teaching Introductory Programming

“…While this report paints a grim picture of the field of computing education research, there has been a recent paradigm shift towards more systematic and focused investigations [Pears and Malmi 2009]. Notable examples include a study by Nikula et al [2011]; building on a clear methodological framework, the Theory of Constraints, a five-year longitudinal study was undertaken in which problems were diagnosed, and suitable interventions (mainly targeting course and student motivational problems) were designed, implemented, and evaluated through pass rates. Remarkable methodological rigor can also be found in a paper by Stefik and Siebert [2013] that reported four large-scale empirical studies involving randomized controlled trials.…”

“…Second, as assessment content and methods differ between institutions, meaningful comparisons using test results are not possible. Third, drop-out rates are influenced by many factors in addition to, or irrespective of, the CS1 course [Nikula et al 2011]. Finally, data on final grades and drop-out rates are collected after the completion of a CS1 course, which means that learning problems are diagnosed when it is already too late for a particular cohort.…”

“…Dropout and failure rates are exacerbated during and between the first and second years of these programs, reaching as high as 30-40% [Beaubouef and Mason 2005] and meaning that even where students begin studying for Computer Science degrees, a high proportion do not go on to graduate. These high attrition rates are associated with considerable failure rates in introductory programming courses and/or disenchantment with programming [McGettrick et al 2004, in Grand Challenges in Computing: Education;Nikula et al 2011]. This disenchantment persists with graduates "expressing a dislike of programming and reluctance to undertake it" [McGettrick et al 2004 p.12; see also Ma et al 2007].…”

Increasing the effectiveness of automated assessment by increasing marking granularity and feedback units

“…Nikula et al [15] present a longitudinal study of a first year programming course restructure, where they introduced an automated marking system, the Virtual Learning Environment, which tested their submissions for correctness as well as tracking student performance metrics. Nikula et al identify student frustration upon receiving feedback that they were not able to act upon once the deadline had passed.…”

Prediction of the change of learners' motivation in programming education for non-computing majors

“…For example, they changed the programming languages, virtual learning environments (VLE), teaching methods, support systems, and the assessment methods in a five-year period. After these broad scale changes, the pass rate had increased from 44% in 2004 to 68% in 2009, and the course atmosphere had turned positive [14].…”