Misleading intuition in algorithmic problem solving

GinatDavid,

doi:10.1145/366413.364530

Cited by 7 publications

(8 citation statements)

References 1 publication

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“…The tasks may be viewed from various angles. Their incorrect solution directions encapsulate familiar novice difficulties, observed in our previous studies [5,6,8]. Their correct solutions require pattern observations that may be reached by creatively varying the tasks.…”

Section: Introductionmentioning

confidence: 70%

“…Unlike experts, novices tend to select a solution direction and "stick to it", with little reconsideration of the train of thought [1,5,8,12,14]. • Misleading intuition.…”

Section: Algorithmic Tasksmentioning

confidence: 99%

“…• Misleading intuition. The solution direction may be invoked by initial intuitive associations, which seem promising at a first glance, but may be misleading [5].…”

Section: Algorithmic Tasksmentioning

confidence: 99%

“…Not much. Previous studies in both mathematics and computer science reveal novice tendencies to "stick" to an initial direction, and solely follow it, to a successful, or an unsuccessful outcome (e.g., [5,8,14]). …”

Section: Introductionmentioning

confidence: 99%

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Learning from wrong and creative algorithm design

GinatDavid

2008

SIGCSE Bull.

Self Cite

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We all are aware of the relevance of examining a task from diverse angles. We also are aware of the potential relevance of learning from one's mistakes. Yet computer science (CS) textbooks and teaching materials fall short in embedding these two notions. In this paper, we present an approach of elaborating divergent thinking in algorithm design, while capitalizing on erroneous solutions. Using a collected set of non-routine algorithmic tasks, we developed and applied a scheme of class activities, in which initial faulty solutions (due to novice tendencies) are carefully examined, and their falsifying inputs and characteristics are used for creative reasoning that yields fruitful outcomes. We present and illustrate our activities, refer to their cognitive aspects, and describe our experience with applying them in (an Introduction-to-Algorithms) class.

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Section: Introductionmentioning

confidence: 70%

“…Unlike experts, novices tend to select a solution direction and "stick to it", with little reconsideration of the train of thought [1,5,8,12,14]. • Misleading intuition.…”

Section: Algorithmic Tasksmentioning

confidence: 99%

“…• Misleading intuition. The solution direction may be invoked by initial intuitive associations, which seem promising at a first glance, but may be misleading [5].…”

Section: Algorithmic Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning from wrong and creative algorithm design

GinatDavid

2008

SIGCSE Bull.

Self Cite

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“…Introductory Computer Science texts rarely present examples illustrating that problem solving has to deal with failure. This is detrimental for our students, as studies indicate that students tend not to vary their approach to problem solving, instead persisting in applying the same techniques regardless of their success [4].…”

Section: Overall Objectivementioning

confidence: 97%

Designing and supporting collaborative learning activities

Falkner

2013

Proceeding of the 44th ACM Technical Symposium on Computer Science Education

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SUMMARYThis session will help participants understand the importance of, and challenges in, introducing collaborative learning within introductory Computer Science curricula. At the University of Adelaide, we have designed our first year curriculum, a sequence of three courses, around collaborative learning -in this session we explore our experiences in developing collaborative activities, collaborative assessment and appropriate training for staff, academic and sessional, involved in collaborative sessions. Based on a seven year programme of designing and undertaking collaborative activities, we will discuss our successes, and our failures, in the use of collaborative learning techniques.

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Developing authentic problem solving skills in introductory computing classes

Falkner

Palmer

2009

Proceedings of the 40th ACM Technical Symposium on Computer Science Education

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The ability to solve problems is the key to developing software, and it is an ability that can be difficult to learn. Introductory Computer Science students are often taught syntax and semantics, along with simple problems designed to develop logical, structured thinking, but authentic problemsolving skills are rarely developed at these early stages.In this paper we describe an approach to introductory computer science education that addresses student engagement through integrating cooperative learning techniques and authentic problem solving processes throughout each aspect of the curriculum.Over a period of 4 years, the introductory computer science course at the University of Adelaide has been modified to support a cooperative learning style. A three-stage methodology has been implemented in place of traditional lectures to achieve this. The process focuses on observing the application of programming techniques, observing problem solving techniques and then applying cooperative problem solving exercises in the classroom. The results from this change in teaching methodology have been an increase in attendance rates at lectures and practical sessions as well as improved learning outcomes as measured by exam results. Student experience surveys show students have greater motivation for learning and believe they have a better understanding of concepts since the changes have occurred.

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Misleading intuition in algorithmic problem solving

Cited by 7 publications

References 1 publication

Learning from wrong and creative algorithm design

Learning from wrong and creative algorithm design

Designing and supporting collaborative learning activities

Developing authentic problem solving skills in introductory computing classes

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