Supporting abstraction processes in problem solving through pattern-oriented instruction

Abstract

Abstraction is a major concept in computer science and serves as a powerful tool in software development. Pattern-oriented instruction (POI) is a pedagogical approach that incorporates patterns in an introductory computer science course in order to structure the learning of algorithmic problem solving. This paper examines abstraction processes in the course of solving an algorithmic problem and highlights three distinct, although interrelated, facets of abstraction: pattern recognition, black-boxing, and structure identification. A study that examined the influence of the POI approach on students' abstraction skills is described; students who learned according to the POI approach were compared with students who learned in a traditional manner with regard to analogical reasoning and problem decomposition and solution, as well as verbal expression. The three facets of abstraction were used to analyze students' abstraction skills and their competency in algorithmic problem solving.

Process-related competence areas to computer science education: An empirical determination

“…The results of this study support the relevant literature, particularly concerning problem solving and algorithmic thinking (Fadi & McHugh, 2001, Muller & Haberman, 2008, system-level-perspective (Gal-Ezer & Zeldes, 2000;Ricks & Jackson, 2009), modelling (Mühling, Hubwieser, & Brinda 2010). They can be used for consolidating available curricular drafts for computer science as a teaching subject at school of the type available in many countries or currently in the state of revision, e.g., for England and Wales, Scotland, the USA, Israel, New Zealand, Germany, India, South Korea, Greece.…”

Dynamic programming - Structure, difficulties and teaching

“…The comparison of problem characteristics is central, and patters are first introduced and later revisited in a different setting. The effect of POI on students' abilities in problem solving, abstraction and analogical reasoning in particular, are investigated by Muller and Haberman [9], [10], [11] and the effect on problem decomposition and solution construction abilities by Muller, Haberman and Ginat in [12].…”

Instructional Methods in STEM Education: A Cross-contextual Study

“…As yet no standard reference for computer science education is available which extensively addresses the application of instructional methods for school. The literature contains descriptions on the application of solving problems (Koffmann & Brinda, 2003), group work (Iron, Alexander, & Alexander, 2004), rich tasks, concept-mapping (Hazzan, Lapidot, & Ragonis, 2011), pattern-oriented instruction (Muller & Haberman, 2008), lab-centered instruction (Titterton, Lewis, & Clancy, 2010), discovery learning and project teaching (Hartmann, Näf, & Reichert, 2006), and visualizations (Fincher & Petre, 2004).…”