Linking Dialogue with Student Modelling to Create an Adaptive Tutoring System for Conceptual Physics

Katz, Shmuel; Albacete, Patricia L.; Chounta, Irene‐Angelica; Jordan, Pamela W.; McLaren, Bruce M.; Zapata‐Rivera, Diego

doi:10.1007/s40593-020-00226-y

Cited by 19 publications

(9 citation statements)

References 120 publications

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“…Other prominent applications in a similar vein include DeepTutor (Rus et al, 2013), a conversational ITS for Newtonian physics, Guru (Olney et al, 2012), a system for high school biology, and ARIES (Cai et al, 2011), an ITS for training college students in scientific reasoning. A more recent example is Rimac (Katz et al, 2011;Albacete et al, 2019;Katz et al, 2021), another system for physics. Based on its assessment of students' responses, Rimac provides feedback and models students' individual knowledge levels in order to adapt to students' individual needs.…”

Section: Conversational Itsmentioning

confidence: 99%

A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring

Bai

Stede

2022

Int J Artif Intell Educ

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Recent years have seen increased interests in applying the latest technological innovations, including artificial intelligence (AI) and machine learning (ML), to the field of education. One of the main areas of interest to researchers is the use of ML to assist teachers in assessing students’ work on the one hand and to promote effective self-tutoring on the other hand. In this paper, we present a survey of the latest ML approaches to the automated evaluation of students’ natural language free-text, including both short answers to questions and full essays. Existing systematic literature reviews on the subject often emphasise an exhaustive and methodical study selection process and do not provide much detail on individual studies or a technical background to the task. In contrast, we present an accessible survey of the current state-of-the-art in student free-text evaluation and target a wider audience that is not necessarily familiar with the task or with ML-based text analysis in natural language processing (NLP). We motivate and contextualise the task from an application perspective, illustrate popular feature-based and neural model architectures and present a selection of the latest work in the area. We also remark on trends and challenges in the field.

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Section: Conversational Itsmentioning

confidence: 99%

A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring

Bai

Stede

2022

Int J Artif Intell Educ

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“…Questions. Regarding the constructed-response tests, short-answer questions were written to allow for better input recognition to deal with the limitations of natural language processing and the potential for inaccurate feedback (Katz et al, 2021). Also, questions in both CBA formats (i.e., constructed and selected) were written through consultation with the course instructors who possess the most accurate and relevant information regarding the topics that students tend to struggle with, the areas where they might require additional assistance, and their expected correct and incorrect responses.…”

Section: Design Of Cbamentioning

confidence: 99%

“…For example, AutoTutor worked better when the content was for qualitative domains (Graesser et al, 2005), whereas ELLA-Math was better at reading responses to questions that needed students to write a number than questions that required students to write words (Lopez et al, 2021). However, many of the existing conversational agents have been designed to support the learning of verbal or qualitative content (e.g., Azevedo et al, 2009;Howard et al, 2017;Katz et al, 2021;Rus et al, 2015;Yang & Zapata-Rivera, 2010), rather than numerical or quantitative content (e.g., Lopez et al, 2021).…”

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confidence: 99%

Conversation-Based Assessments in Education: Design, Implementation, and Cognitive Walkthroughs for Usability Testing

Yildirim-Erbasli

Bulut

Epp

et al. 2023

Journal of Educational Technology Systems

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Conversational agents have been widely used in education to support student learning. There have been recent attempts to design and use conversational agents to conduct assessments (i.e., conversation-based assessments: CBA). In this study, we developed CBA with constructed and selected-response tests using Rasa—an artificial intelligence-based tool. CBA was deployed via Google Chat to support formative assessment. We evaluated (1) its performance in answering students’ responses and (2) its usability with cognitive walkthroughs conducted by external evaluators. CBA with constructed-response tests consistently matched student responses to the appropriate conversation paths in most cases. In comparison, CBA with selected-response tests demonstrated perfect accuracy between system design and implementation. A cognitive walkthrough of CBA showed its usability as well as several potential issues that could be improved. Participating students did not experience these issues, however, we reported them to help researchers, designers, and practitioners improve the assessment experience for students using CBA.

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“…The scaffolding approach assists students' learning, through a self-determined pace, to plan, to monitor, and to measure the extent to which they have been progressing in their physics learning. Diversity in physics has been promoted through other studies, including the adaptive tutoring system (ATS) [16,17], the computerized adaptive test (CAT) [18], and employing machine learning (ML) algorithms [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Physics Teachers’ Perceptions about Their Judgments within Differentiated Learning Environments: A Case for the Implementation of Technology

2022

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There is a national shift in the new Indonesian curriculum towards employing differentiated learning approaches in addressing the diversity of students’ needs and abilities. A teachers’ judgment evidently corresponds to the duty required of physics teachers to monitor their students at an individual level. Within the context of Indonesian physics education research (PER), empirical study addressing this subject is still lacking. To fill this gap, eight Indonesian physics teachers’ experiences and limitations about their judgments within differentiated learning environments have been investigated through phenomenological study. Physics teachers were voluntarily recruited after they declared their endorsement and personal consent to participate in the study. Our participants were distributed over several teaching experiences, geographic regions, and information and communication technology (ICT) experiences. The latter experience might be taken into account since, through this study, upcoming developmental research will be projected on engaging recent technological approaches to address the limitations of teachers’ judgments. Online semi-structured interviews (~50 min) were conducted by the first author to all physics teachers involved. Other authors contributed in reviewing the interview protocol and training the first author’s pilot interview. The model of teachers’ thought and action, from Clark and Peterson, was implemented to uncover physics teachers’ experiences and limitations in making judgments within a diverse group of students. The findings revealed that physics teachers have conceded that they should dynamically adapt the learning process in order to meet heterogeneous students’ performances. Personal observation has mainly informed teachers in identifying students’ differences. After students have been identified, the teachers creatively designed learning transformations to accommodate the wide spectrum of students’ abilities. Nevertheless, several limitations were discovered as being encountered by physics teachers, particularly in terms of judgments’ equity and accuracy, as well as the teachers’ workloads. To overcome this, the teachers indicated various and supportive attitudes about technological implementations that would assist their judgments. Ideas for technological development were provided to address identified obstacles during the teachers’ judgments.

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Linking Dialogue with Student Modelling to Create an Adaptive Tutoring System for Conceptual Physics

Cited by 19 publications

References 120 publications

A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring

A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring

Conversation-Based Assessments in Education: Design, Implementation, and Cognitive Walkthroughs for Usability Testing

Physics Teachers’ Perceptions about Their Judgments within Differentiated Learning Environments: A Case for the Implementation of Technology

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