Interactive Correction and Recommendation for Computer Language Learning and Training

Pahl, Claus; Kenny, Claire

doi:10.1109/tkde.2008.144

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…For example, Pahl and Kenny. presented an approach to correcting, providing feedback and presenting recommended questions for programming courses [14]. This approach uses language grammars and syntax trees to process the feedback.…”

Section: Releated Work a Adaptive E-learningmentioning

confidence: 99%

Reverse SQL Question Generation Algorithm in the DBLearn Adaptive E-Learning System

2019

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Using a traditional e-learning system, when teaching structured query language (SQL) queries in classical classrooms help instructors, to improve the students' SQL skills and learning effectiveness. However several problems in using e-learning as a teaching and learning assistant remain -such as difficulties in differences in learning ability and knowledge level. We solved these problems by applying an adaptation module to our e-learning system. However, we still found it required considerable effort to create enough exercises to make the adaptation effective enough. So, we developed a novel automatic question generating algorithm, named Reverse SQL Question Generation Algorithm (RSQLG), to automatically generate exercises (including both answer and question) from a source database. RSQLG reverses the traditional manual process used previously by instructors. Instead of creating questions and answers for them, RSQLG creates the answers first. The generated exercises are presented to students by applying question adaptation methodology based on student knowledge level in each supported learning objective. We evaluated the learning effectiveness of our approach by using outcome-based learning. After post-test to pre-test scores were compared, we found students using our system improved their scores by 26%. Consequently, the adaptive e-learning framework using RSQLG could be applied in any adaptive or traditional e-learning for a database course to benefit the instructors leading to less effort in exercise management and to improve the learning outcome from the students allowing as much practice as they need.INDEX TERMS E-learning, adaptive system, automated question generating algorithm, computer-aided instruction, SQL learning.

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Section: Releated Work a Adaptive E-learningmentioning

confidence: 99%

Reverse SQL Question Generation Algorithm in the DBLearn Adaptive E-Learning System

2019

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“…{ (1, 1), (1, 3), (1, 5), (2, 2), (2, 4), (2,6), (3,1), (3,3), (3,5) An example problem, the correct event set representation, and an applicable semantic constraint are shown in Figure 5. In the example, two distinct dice are rolled and their upper faces are observed.…”

Section: Problemmentioning

confidence: 99%

Constraint-based Student Modelling in Probability Story Problems with Scaffolding Techniques

Khodeir

Wanas

Elazhary

2018

Int. J. Emerg. Technol. Learn.

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Abstract-Constraint-Based student Modeling (CBM) is an important technique employed in intelligent tutoring systems to model student knowledge to provide relevant assistance. This paper introduces the Math Story Problem Tutor (MAST), a Web-based intelligent tutoring system for probability story problems, which is able to generate problems of different contexts, types and difficulty levels for self-paced learning. Constraints in MAST are specified at a lowlevel of granularity to allow fine-grained diagnosis of the student error. Furthermore, MAST extends CBM to address errors due to misunderstanding of the narrative story. It can locate and highlight keywords that may have been overlooked or misunderstood leading to an error. This is achieved by utilizing the roles and syntaxes of the problem segments and the semantic descriptions of the keywords that are defined through the Natural Language Generation (NLG) methods deployed in the story problem generation. MAST also integrates CBM with scaffolding questions and feedback to provide various forms of help and guidance to the student. This allows the student to discover and correct any errors in his/her solution. The tutoring effect of MAST has been evaluated empirically using different tests (paired samples t-test, normalized knowledge gain, Mann-Whitney U test, and power learning curves). The results demonstrated a positive effect of MAST on improving the exam scores, and the normalized knowledge gain. Additionally, keyword highlighting of errors integrated in MAST has been shown to speed up learning and improve reduction of average percentage of violated constraints along the learning curve. This suggests that using various forms of assistance can help speedup learning, and that welldesigned tutoring systems providing relevant help can be superior to traditional approaches.

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“…Recently curriculum sequencing has been broadened to consider other individual traits that might affect learning, such as the learner's emotions (Lehman et al 2012;Mao & Li 2010), personality type (Leontidis, Halatsis & Grigoriadou 2011) and learning style (Popescu 2010).  Intelligent solution analysis enables ITS to give detailed feedback on incomplete or erroneous solutions, helping students learn from their mistakes (Pahl & Kenny 2009).  Problem solving support embraces the constructivist approach to teaching used by human tutors to intelligently prompt learners to solve problems themselves, constructing their own knowledge and thus encouraging a deeper understanding of a topic (Redondo, Bravo, Ortega & Verdejo 2007).…”

Section: Intelligent Tutoring Systemsmentioning

confidence: 99%

An adaptation algorithm for an intelligent natural language tutoring system

Latham

Crockett

McLean

2014

Computers & Education

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The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the Felder-Silverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.

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Interactive Correction and Recommendation for Computer Language Learning and Training

Cited by 10 publications

References 22 publications

Reverse SQL Question Generation Algorithm in the DBLearn Adaptive E-Learning System

Reverse SQL Question Generation Algorithm in the DBLearn Adaptive E-Learning System

Constraint-based Student Modelling in Probability Story Problems with Scaffolding Techniques

An adaptation algorithm for an intelligent natural language tutoring system

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