Generating story problems via controlled parameters in a web-based intelligent tutoring system

Abstract

Purpose The purpose of this paper is to present an algorithm to generate story problems via controlled parameters in the domain of mathematics. The generation process is performed in the problem generation module in the context of an intelligent tutoring system suggested in this paper. Controlling the question parameters allows for adapting the generated questions according to the specific student needs. Story problems are selected since they are one of the most important types of problems in mathematics, as they help train students to apply their knowledge to real-world problems. Such problems target improving different student’s skills including literacy skills through reading the problem, recognizing the embedded mathematical information, and applying the required arithmetic operators. Design/methodology/approach Natural language generation (NLG) techniques are used to control the difficulty level of the generated story problem header in addition to effecting variations from the natural language point of view. The proposed NLG technique is based on different separated knowledge categories to provide flexibility in the generation process and allow porting the module to other contexts, domains, and to other natural languages without a complete redesign. Findings The approach has been empirically evaluated, and the results show that the generated problems are sound, clear, and naturally readable. This is in addition to the usability of the tutoring system itself. Research limitations/implications The generation technique is confined to the problem described using rhetorical schemas. Nevertheless, it can generate any problem provided that the rhetorical schema is available. Originality/value Most story problems generation systems limit the variation of the story problems to formulating the sentences that describe the story problem and the associated mathematical operations. In contrast, this paper presents a story problems generation technique that allows variations in the structure of the narrative story as well as the context, sentences, wordings, and mathematical operations. This variability allows assessing different student skills along different dimensions with gradually increasing difficulty levels.

A Systematic Review of Automatic Question Generation for Educational Purposes

“…Despite the growth in AQG, only 14 studies have dealt with difficulty. Eight of these studies focus on the difficulty of questions belonging to a particular domain, such as mathematical word problems (Wang and Su 2016;Khodeir et al 2018), geometry questions (Singhal et al 2016), vocabulary questions (Susanti et al 2017a), reading comprehension questions (Gao et al 2018), DFA problems (Shenoy et al 2016), code-tracing questions (Thomas et al 2019), and medical case-based questions Kurdi et al 2019). The remaining six focus on controlling the difficulty of non-domain-specific questions (Lin et al 2015;Alsubait et al 2016;Kurdi et al 2017;Faizan and Lohmann 2018;Faizan et al 2017;Seyler et al 2017;Kumar 2015a, 2017a;Vinu et al 2016;Kumar 2017b, 2015b).…”

“…Difficulty control was validated by checking agreement between predicted difficulty and expert prediction in Vinu and Kumar (2015b), Alsubait et al (2016), Seyler et al (2017), Khodeir et al (2018), and Leo et al (2019), by checking agreement between predicted difficulty and student performance in Alsubait et al (2016), Susanti et al (2017a), Lin et al (2015), Wang and Su (2016), Leo et al (2019), and Thomas et al (2019), by employing automatic solvers in Gao et al (2018), or by asking experts to complete a survey after using the tool (Singhal et al 2016). Expert reviews and mock exams are equally represented (seven studies each).…”

“…Wang et al (2018) claimed that these metrics can be used as initial, inexpensive, large-scale indicators of the fluency and relevancy of questions. Other researchers investigated whether machine-generated questions are indistinguishable from human-authored questions by mixing both types and asking experts about the source of each question Susanti et al 2015;Khodeir et al 2018). Some researchers evaluated their approaches by investigating the ability of the approach to assemble human-authored distractors.…”

“…#Q = no of evaluated questions; #P = no of participants (whether S = student(s), E = expert(s), C = co-worker(s) or A = author(s)); avg. = average; NR = not reported in the paper; NC = not clear; NA = not applicable; * = not reported but calculated based on provided data; and (+) = refer to the paper for extra information about the results or the context of the study Reference International Journal of Artificial Intelligence in Education (2020) 30:121-204 Khodeir et al (2018Khodeir et al ( , 2018 math 25 4 E 82% questions were thought to be human-authored 3 categories (system-generated; human-generated; unsure) Susanti et al (2015Susanti et al ( , 2016Susanti et al ( , 2017a language 22 7 E 45% questions were thought to be human-authored binary choice (human-generate; machine-generated) language 69 364 C 67% questions were thought to be human-authored Overlap with human generated questions Shirude et al (2015) generic NR 12 E 63.15% types of questions generated by the human are covered by the generator NR Vinu and Kumar (2015a generic NR NR recall = 43% to 81%, precision = 72% to 93% (+) Liu et al (2017) language (RC) 600 3 E recall = 64%, precision = 69% (+) NR Jouault et al (2015aJouault et al ( , b, 2016aJouault et al ( , b, 2017 history 69 1 E 84% of the human-authored questions are covered by auto-generated questions coverages means that both questions cover the same knowledge…”

An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning

“…These learners have no freedom to propose new exercises that they might come up with. Recent research has focused on open learning environments, where the set of exercises is not limited to a predefined set, or where the learners can create new exercises [20] , [21] , [22] , [23] , [24] , [25] , [26] . These systems provide a language that enables the generation of exercises and the communication with the learners, which may be a standard language (e.g.…”