A Motivation Guided Holistic Rehabilitation of the First Programming Course

Author:

Nikula Uolevi1,Gotel Orlena2,Kasurinen Jussi1

Affiliation:

1. Lappeenranta University of Technology

2. Independent Researcher

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Education,General Computer Science

Reference106 articles.

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2. More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing

3. Back to Pascal: Retro but not backwards;Becker K.;J. Comput. Sci. Coll.,2002

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