Pub Date : 2022-11-22DOI: 10.1080/15391523.2022.2148786
B. Schäffer, Fabio Roman Lieder
Abstract This article highlights teaching and learning in reconstructive research supported by artificial intelligence (AI) and machine interpretation in particular. The focus is whether the traditional teaching of methodological competence through research workshops can be supplemented with artificial intelligence (natural language processing, NLP) implemented in computer-assisted qualitative data analysis software (CAQDAS). A case study shows that AI models can be trained to interpret texts. Thus, distributed interpretation by humans and AI becomes possible, opening up new possibilities for teaching qualitative methods. How people deal with these new possibilities is presented based on an explorative evaluation of a group discussion with young researchers. Finally, this contribution discusses the possibilities and limits of this new form of interpretation together with a machine.
{"title":"Distributed interpretation – teaching reconstructive methods in the social sciences supported by artificial intelligence","authors":"B. Schäffer, Fabio Roman Lieder","doi":"10.1080/15391523.2022.2148786","DOIUrl":"https://doi.org/10.1080/15391523.2022.2148786","url":null,"abstract":"Abstract This article highlights teaching and learning in reconstructive research supported by artificial intelligence (AI) and machine interpretation in particular. The focus is whether the traditional teaching of methodological competence through research workshops can be supplemented with artificial intelligence (natural language processing, NLP) implemented in computer-assisted qualitative data analysis software (CAQDAS). A case study shows that AI models can be trained to interpret texts. Thus, distributed interpretation by humans and AI becomes possible, opening up new possibilities for teaching qualitative methods. How people deal with these new possibilities is presented based on an explorative evaluation of a group discussion with young researchers. Finally, this contribution discusses the possibilities and limits of this new form of interpretation together with a machine.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":"55 1","pages":"111 - 124"},"PeriodicalIF":5.1,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49360410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.1080/15391523.2022.2142872
Christian W. F. Mayer, Sabrina Ludwig, Steffen Brandt
Abstract This study investigates the potential of automated classification using prompt-based learning approaches with transformer models (large language models trained in an unsupervised manner) for a domain-specific classification task. Prompt-based learning with zero or few shots has the potential to (1) make use of artificial intelligence without sophisticated programming skills and (2) make use of artificial intelligence without fine-tuning models with large amounts of labeled training data. We apply this novel method to perform an experiment using so-called zero-shot classification as a baseline model and a few-shot approach for classification. For comparison, we also fine-tune a language model on the given classification task and conducted a second independent human rating to compare it with the given human ratings from the original study. The used dataset consists of 2,088 email responses to a domain-specific problem-solving task that were manually labeled for their professional communication style. With the novel prompt-based learning approach, we achieved a Cohen’s kappa of .40, while the fine-tuning approach yields a kappa of .59, and the new human rating achieved a kappa of .58 with the original human ratings. However, the classifications from the machine learning models have the advantage that each prediction is provided with a reliability estimate allowing us to identify responses that are difficult to score. We, therefore, argue that response ratings should be based on a reciprocal workflow of machine raters and human raters, where the machine rates easy-to-classify responses and the human raters focus and agree on the responses that are difficult to classify. Further, we believe that this new, more intuitive, prompt-based learning approach will enable more people to use artificial intelligence.
{"title":"Prompt text classifications with transformer models! An exemplary introduction to prompt-based learning with large language models","authors":"Christian W. F. Mayer, Sabrina Ludwig, Steffen Brandt","doi":"10.1080/15391523.2022.2142872","DOIUrl":"https://doi.org/10.1080/15391523.2022.2142872","url":null,"abstract":"Abstract This study investigates the potential of automated classification using prompt-based learning approaches with transformer models (large language models trained in an unsupervised manner) for a domain-specific classification task. Prompt-based learning with zero or few shots has the potential to (1) make use of artificial intelligence without sophisticated programming skills and (2) make use of artificial intelligence without fine-tuning models with large amounts of labeled training data. We apply this novel method to perform an experiment using so-called zero-shot classification as a baseline model and a few-shot approach for classification. For comparison, we also fine-tune a language model on the given classification task and conducted a second independent human rating to compare it with the given human ratings from the original study. The used dataset consists of 2,088 email responses to a domain-specific problem-solving task that were manually labeled for their professional communication style. With the novel prompt-based learning approach, we achieved a Cohen’s kappa of .40, while the fine-tuning approach yields a kappa of .59, and the new human rating achieved a kappa of .58 with the original human ratings. However, the classifications from the machine learning models have the advantage that each prediction is provided with a reliability estimate allowing us to identify responses that are difficult to score. We, therefore, argue that response ratings should be based on a reciprocal workflow of machine raters and human raters, where the machine rates easy-to-classify responses and the human raters focus and agree on the responses that are difficult to classify. Further, we believe that this new, more intuitive, prompt-based learning approach will enable more people to use artificial intelligence.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":" 10","pages":"125 - 141"},"PeriodicalIF":5.1,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41251892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.1080/15391523.2022.2142874
Li-juan Cheng, John Hampton, Swapna Kumar
{"title":"Engaging students via synchronous peer feedback in a technology-enhanced learning environment","authors":"Li-juan Cheng, John Hampton, Swapna Kumar","doi":"10.1080/15391523.2022.2142874","DOIUrl":"https://doi.org/10.1080/15391523.2022.2142874","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45029966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.1080/15391523.2022.2134236
Matthew L. Wilson
{"title":"Topics, author profiles, and collaboration networks in the Journal of Research on Technology in Education: A bibliometric analysis of 20 years of research","authors":"Matthew L. Wilson","doi":"10.1080/15391523.2022.2134236","DOIUrl":"https://doi.org/10.1080/15391523.2022.2134236","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43609555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-27DOI: 10.1080/15391523.2022.2135144
Selena Steinberg, M. Gresalfi, Lauren Vogelstein, C. Brady
{"title":"Coding choreography: Understanding student responses to representational incompatibilities between dance and programming","authors":"Selena Steinberg, M. Gresalfi, Lauren Vogelstein, C. Brady","doi":"10.1080/15391523.2022.2135144","DOIUrl":"https://doi.org/10.1080/15391523.2022.2135144","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":"1 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41771079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-26DOI: 10.1080/15391523.2022.2139026
Shuqiong Luo, Di Zou
{"title":"A systematic review of research on technological, pedagogical, and content knowledge (TPACK) for online teaching in the humanities","authors":"Shuqiong Luo, Di Zou","doi":"10.1080/15391523.2022.2139026","DOIUrl":"https://doi.org/10.1080/15391523.2022.2139026","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45918135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-07DOI: 10.1080/15391523.2022.2128480
Lucrezia Crescenzi-Lanna
Abstract This paper presents a systematic literature review of artificial intelligence (AI)-supported teaching and learning in early childhood. The focus is on human–machine cooperation in education. International evidence and associated problems with the reciprocal contributions of humans and machines are presented and discussed, as well as future horizons regarding AI research in early education. Also, the ethical implications of applying machine learning, deep learning and learning analytics in early childhood education are considered. The method adopted has five steps: identification of the research, evaluation and selection of the literature, data extraction, synthesis, and results. The results shown that AI applications still present limitations in terms of the challenges encountered in early childhood education and data privacy and protection policies.
{"title":"Literature review of the reciprocal value of artificial and human intelligence in early childhood education","authors":"Lucrezia Crescenzi-Lanna","doi":"10.1080/15391523.2022.2128480","DOIUrl":"https://doi.org/10.1080/15391523.2022.2128480","url":null,"abstract":"Abstract This paper presents a systematic literature review of artificial intelligence (AI)-supported teaching and learning in early childhood. The focus is on human–machine cooperation in education. International evidence and associated problems with the reciprocal contributions of humans and machines are presented and discussed, as well as future horizons regarding AI research in early education. Also, the ethical implications of applying machine learning, deep learning and learning analytics in early childhood education are considered. The method adopted has five steps: identification of the research, evaluation and selection of the literature, data extraction, synthesis, and results. The results shown that AI applications still present limitations in terms of the challenges encountered in early childhood education and data privacy and protection policies.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":"55 1","pages":"21 - 33"},"PeriodicalIF":5.1,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41944268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1080/15391523.2022.2121345
P. Rich, S. Bartholomew, David Daniel, K. Dinsmoor, Meagan Nielsen, Connor Reynolds, Meg Swanson, Ellyse Winward, Jessica Yauney
{"title":"Trends in tools used to teach computational thinking through elementary coding","authors":"P. Rich, S. Bartholomew, David Daniel, K. Dinsmoor, Meagan Nielsen, Connor Reynolds, Meg Swanson, Ellyse Winward, Jessica Yauney","doi":"10.1080/15391523.2022.2121345","DOIUrl":"https://doi.org/10.1080/15391523.2022.2121345","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42780040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1080/15391523.2022.2123872
Cheng-Yu Chung, I-Han Hsiao, Yi-ling Lin
Abstract Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without advanced technological support. This study proposes a knowledge-based PQG model that aims to help the instructor generate new programming questions and expand the assessment items by the Local Knowledge Graph and Abstract Syntax Tree. A group of experienced instructors was recruited to evaluate the PQG model and expressed significantly positive feedback on the generated questions.
{"title":"AI-assisted programming question generation: Constructing semantic networks of programming knowledge by local knowledge graph and abstract syntax tree","authors":"Cheng-Yu Chung, I-Han Hsiao, Yi-ling Lin","doi":"10.1080/15391523.2022.2123872","DOIUrl":"https://doi.org/10.1080/15391523.2022.2123872","url":null,"abstract":"Abstract Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without advanced technological support. This study proposes a knowledge-based PQG model that aims to help the instructor generate new programming questions and expand the assessment items by the Local Knowledge Graph and Abstract Syntax Tree. A group of experienced instructors was recruited to evaluate the PQG model and expressed significantly positive feedback on the generated questions.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":"55 1","pages":"94 - 110"},"PeriodicalIF":5.1,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41923932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.1080/15391523.2022.2121343
P. Prinsloo, Sharon Slade, M. Khalil
Abstract This article seeks to explore different combinations of human and Artificial Intelligence (AI) decision-making in the context of distributed learning. Distributed learning institutions face specific challenges such as high levels of student attrition and ensuring quality, cost-effective student support at scale using a range of technologies, such as AI. While there is an expanding body of research on AI in education (AIEd), this conceptual article proposes that combinations of human-algorithmic decision-making systems need careful and critical consideration, not only for their potential, but also for their appropriateness and ethical considerations. We operationalize a framework designed to consider robot autonomy at four key events in students’ learning journeys, namely (1) admission and registration; (2) student advising and support; (3) augmenting pedagogy; and (4) formative and summative assessment. We conclude the article by providing pointers for operationalizing options in human-algorithmic decision-making in distributed learning contexts.
{"title":"At the intersection of human and algorithmic decision-making in distributed learning","authors":"P. Prinsloo, Sharon Slade, M. Khalil","doi":"10.1080/15391523.2022.2121343","DOIUrl":"https://doi.org/10.1080/15391523.2022.2121343","url":null,"abstract":"Abstract This article seeks to explore different combinations of human and Artificial Intelligence (AI) decision-making in the context of distributed learning. Distributed learning institutions face specific challenges such as high levels of student attrition and ensuring quality, cost-effective student support at scale using a range of technologies, such as AI. While there is an expanding body of research on AI in education (AIEd), this conceptual article proposes that combinations of human-algorithmic decision-making systems need careful and critical consideration, not only for their potential, but also for their appropriateness and ethical considerations. We operationalize a framework designed to consider robot autonomy at four key events in students’ learning journeys, namely (1) admission and registration; (2) student advising and support; (3) augmenting pedagogy; and (4) formative and summative assessment. We conclude the article by providing pointers for operationalizing options in human-algorithmic decision-making in distributed learning contexts.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":"55 1","pages":"34 - 47"},"PeriodicalIF":5.1,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46540501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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