Assistance and Feedback Mechanism in an Intelligent Tutoring System for Teaching Conversion of Natural Language into Log
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Assistance and Feedback Mechanism in an Intelligent Tutoring System for Teaching Conversion of Natural Language into Logic Isidoros Perikos 1 & Foteini Grivokostopoulou 1 & Ioannis Hatzilygeroudis 1
# International Artificial Intelligence in Education Society 2017
Abstract Logic as a knowledge representation and reasoning language is a fundamental topic of an Artificial Intelligence (AI) course and includes a number of sub-topics. One of them, which brings difficulties to students to deal with, is converting natural language (NL) sentences into first-order logic (FOL) formulas. To assist students to overcome those difficulties, we developed the NLtoFOL system and equipped it with a strong assistance and feedback mechanism. In this work, first, we present that feedback mechanism. The mechanism can provide assistance before an answer is submitted, if requested, but mainly it provides assistance after an answer is submitted. To that end, it characterizes the answer in terms of completeness and accuracy to determine the level of incorrectness, based on an answer categorization scheme, introduced in this paper. The automatically generated natural language feedback sequences grow from general to specific and can include statements on a student’s metacognitive state. Feedback is provided as natural language sentences automatically generated through a templatebased natural language generation mechanism. Second, we present an extensive evaluation of the effectiveness of the assistance and feedback mechanism on students’ learning. The evaluation of feedback with students showed that full feedback sequences lead to greater learning gains than sequences consisting of only flag feedback and
* Isidoros Perikos [email protected] Foteini Grivokostopoulou [email protected] Ioannis Hatzilygeroudis [email protected]
1
Department of Computer Engineering & Informatics, School of Engineering, University of Patras, 26504 Patras, Hellas, Greece
Int J Artif Intell Educ
bottom-out hints (n = 226), and that generic, template-based feedback sequences are comparable to the utility of problem-specific hints generated by human tutors (n = 120). Keywords Feedback framework . Student assistance . Answer categorization . Feedback sequencing . Error categorization . Feedback effectiveness evaluation . Learning analytics
Introduction A great variety of different learning systems exist that formulate what is called elearning, such as Learning Management Systems (LMS), Computer Assisted Instruction (CAI) systems, Intelligent Tutoring Systems (ITS), Adaptive Educational Hypermedia Systems (AEHSs). ITSs constitute a popular type of educational systems that are becoming a fundamental means of education delivery, leading to impressive improvement in student learning (Aleven et al. 2009). Their main characteristics are that they provide instructions and feedback tailored to the learners and perform their main tasks based on Artificial Intelligence methods. The development of tutoring systems and personalized learning environments,
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