Teaching the tacit knowledge of programming to noviceswith natural language tutoring

Lane, H. Chad; VanLehn, Kurt

doi:10.1080/08993400500224286

Cited by 76 publications

(40 citation statements)

References 10 publications

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“…Topic modules in which students used the dialogue-based tutor yielded significantly improved scores from pretest to posttest over those topics in which students used the CAI version. Because the tutoring condition was much more interactive than the CAI condition, the results align with previous research suggesting that the increased interaction in the tutoring condition facilitates learning (Graesser et al, 2003;Lane & VanLehn, 2005;Wood, Wood, & Middleton, 1978). Studies of this interaction hypothesis, however, are not conclusive, and recent research (VanLehn et al, 2007) has suggested that interaction is most effective when students are learning material that is above their current preparation level (as when novices are learning material written for intermediates).…”

Section: Discussionsupporting

confidence: 72%

Research Methods Tutor: Evaluation of a dialogue-based tutoring system in the classroom

Arnott

Hastings

Allbritton

2008

Behavior Research Methods

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Research Methods Tutor (RMT) is a dialogue-based intelligent tutoring system for use in conjunction with undergraduate psychology research methods courses. RMT includes five topics that correspond to the curriculum of introductory research methods courses: ethics, variables, reliability, validity, and experimental design. We evaluated the effectiveness of the RMT system in the classroom using a nonequivalent control group design. Students in three classes (n 83) used RMT, and students in two classes (n 53) did not use RMT. Results indicated that the use of RMT yielded strong learning gains of 0.75 standard deviations above classroom instruction alone. Further, the dialogue-based tutoring condition of the system resulted in higher gains than did the textbook-style condition (CAI version) of the system. Future directions for RMT include the addition of new topics and tutoring elements.

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Section: Discussionsupporting

confidence: 72%

Research Methods Tutor: Evaluation of a dialogue-based tutoring system in the classroom

Arnott

Hastings

Allbritton

2008

Behavior Research Methods

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“…Different approaches have been proposed to improve the programming skills of novice programmers [3]- [6]. It includes creating visualizations building block from multimedia tools [7], goal/plan analysis programming strategies [8], teaching machine [9] and natural language tutor -ProPL [10]. Winslow [11] pointed out -that novice programmers know the syntax and semantics of individual statements, but they do not know how to combine these features into valid programs‖.…”

Section: Introductionmentioning

confidence: 99%

“…Novice programmers generally start writing their programs before making an attempt to plan them [10].…”

Section: Introductionmentioning

confidence: 99%

A Self Review and External Review Model for Teaching and Assessing Novice Programmers

Iqbal¹,

Harsh²

2013

IJIET

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Abstract-Teaching and assessing programming skills in novice programmers is a challenging and difficult task. Most of the introductory programming courses emphasized in teaching programming knowledge (syntax and semantics of programming language to the novice programmers) instead of giving equal attention and time to problem solving strategies, analysis of output(results) and improvement in programming practices . Hence this paper makes an attempt to introduce ADRI model (well known quality assurance model for self review and external review) approach in preparing teaching and assessment materials in programming courses to novice programmers so that it ensures that programming knowledge and problem solving strategies are properly incorporated and practiced in teaching and assessing materials for novice programmers.Index Terms-Novice programmers, ADRI model, programming knowledge, problem solving strategies.

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“…That means, this criterion selects only systems that focus on the coding phase of programming and opts out those systems that support students pursue other learning objectives in the analysis, planning/design, and test phases of programming, for example, [19][20][21][22].…”

Section: Methodsmentioning

confidence: 99%

A Classification of Adaptive Feedback in Educational Systems for Programming

2016

Systems

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Over the last three decades, many educational systems for programming have been developed to support learning/teaching programming. In this paper, feedback types that are supported by existing educational systems for programming are classified. In order to be able to provide feedback, educational systems for programming deployed various approaches to analyzing students' programs. This paper identifies analysis approaches for programs and introduces a classification for adaptive feedback supported by educational systems for programming. The classification of feedback is the contribution of this paper.

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Teaching the tacit knowledge of programming to noviceswith natural language tutoring

Cited by 76 publications

References 10 publications

Research Methods Tutor: Evaluation of a dialogue-based tutoring system in the classroom

Research Methods Tutor: Evaluation of a dialogue-based tutoring system in the classroom

A Self Review and External Review Model for Teaching and Assessing Novice Programmers

A Classification of Adaptive Feedback in Educational Systems for Programming

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