Search

Your search keyword '"Gašević, Dragan"' showing total 1,065 results
Start Over Author "Gašević, Dragan"

Refine your results

1,065 results on '"Gašević, Dragan"'

106. Leadership and Maturity: How Do They Affect Learning Analytics Adoption in Latin America? : A Cross-Case Analysis in Four Latin American Universities

Author

Hilliger, Isabel, Pérez-Sanagustín, Mar, Pérez-Álvarez, Ronald, Henríquez, Valeria, Guerra, Julio, Zuñiga-Prieto, Miguel Ángel, Ortiz-Rojas, Margarita, Tsai, Yi-Shan, Gasevic, Dragan, Muñoz-Merino, Pedro J., Broos, Tom, De Laet, Tinne, Ifenthaler, Dirk, Series Editor, and Gibson, David, Series Editor

Published
2020
Full Text
View/download PDF

107. Investigating the Role of Politeness in Human-Human Online Tutoring

Author

Lin, Jionghao, Lang, David, Xie, Haoran, Gašević, Dragan, Chen, Guanliang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bittencourt, Ig Ibert, editor, Cukurova, Mutlu, editor, Muldner, Kasia, editor, Luckin, Rose, editor, and Millán, Eva, editor

Published
2020
Full Text
View/download PDF

108. Expediting Support for Social Learning with Behavior Modeling

Author

Jo, Yohan, Tomar, Gaurav, Ferschke, Oliver, Rose, Carolyn P., and Gasevic, Dragan

Subjects
Computer Science - Computers and Society
Abstract

An important research problem for Educational Data Mining is to expedite the cycle of data leading to the analysis of student learning processes and the improvement of support for those processes. For this goal in the context of social interaction in learning, we propose a three-part pipeline that includes data infrastructure, learning process analysis with behavior modeling, and intervention for support. We also describe an application of the pipeline to data from a social learning platform to investigate appropriate goal-setting behavior as a qualification of role models. Students following appropriate goal setters persisted longer in the course, showed increased engagement in hands-on course activities, and were more likely to review previously covered materials as they continued through the course. To foster this beneficial social interaction among students, we propose a social recommender system and show potential for assisting students in interacting with qualified goal setters as role models. We discuss how this generalizable pipeline can be adapted for other support needs in online learning settings., Comment: in The 9th International Conference on Educational Data Mining, 2016

Published
2016

116. What is the Effect of a Dominant Code in an Epistemic Network Analysis?

Author

Ferreira Mello, Rafael, Gašević, Dragan, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Eagan, Brendan, editor, Misfeldt, Morten, editor, and Siebert-Evenstone, Amanda, editor

Published
2019
Full Text
View/download PDF

117. The Influence of Discipline on Teachers’ Knowledge and Decision Making

Author

Phillips, Michael, Kovanović, Vitomir, Mitchell, Ian, Gašević, Dragan, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Eagan, Brendan, editor, Misfeldt, Morten, editor, and Siebert-Evenstone, Amanda, editor

Published
2019
Full Text
View/download PDF

118. Synergy: A Web-Based Tool to Facilitate Dialogic Peer Feedback

Author

Er, Erkan, Dimitriadis, Yannis, Gašević, Dragan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Scheffel, Maren, editor, Broisin, Julien, editor, Pammer-Schindler, Viktoria, editor, Ioannou, Andri, editor, and Schneider, Jan, editor

Published
2019
Full Text
View/download PDF

119. Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques

Author

Ahmad Uzir, Nora’ayu, Gašević, Dragan, Matcha, Wannisa, Jovanović, Jelena, Pardo, Abelardo, Lim, Lisa-Angelique, Gentili, Sheridan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Scheffel, Maren, editor, Broisin, Julien, editor, Pammer-Schindler, Viktoria, editor, Ioannou, Andri, editor, and Schneider, Jan, editor

Published
2019
Full Text
View/download PDF

120. Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches

Author

Matcha, Wannisa, Gašević, Dragan, Ahmad Uzir, Nora’ayu, Jovanović, Jelena, Pardo, Abelardo, Maldonado-Mahauad, Jorge, Pérez-Sanagustín, Mar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Scheffel, Maren, editor, Broisin, Julien, editor, Pammer-Schindler, Viktoria, editor, Ioannou, Andri, editor, and Schneider, Jan, editor

Published
2019
Full Text
View/download PDF

121. Policy Matters: Expert Recommendations for Learning Analytics Policy

Author

Scheffel, Maren, Tsai, Yi-Shan, Gašević, Dragan, Drachsler, Hendrik, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Scheffel, Maren, editor, Broisin, Julien, editor, Pammer-Schindler, Viktoria, editor, Ioannou, Andri, editor, and Schneider, Jan, editor

Published
2019
Full Text
View/download PDF

122. I Wanna Talk Like You: Speaker Adaptation to Dialogue Style in L2 Practice Conversation

Author

Sinclair, Arabella J., Ferreira, Rafael, Gašević, Dragan, Lucas, Christopher G., Lopez, Adam, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
Full Text
View/download PDF

123. A Comparative Study on Question-Worthy Sentence Selection Strategies for Educational Question Generation

Author

Chen, Guanliang, Yang, Jie, Gasevic, Dragan, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
Full Text
View/download PDF

126. Automated Thematic Coherence Scoring of Student Essays Written in Portuguese

Author

Pacheco, Rafael, primary, Rodrigues, Luiz, additional, Lins, Lucas, additional, Miranda, Péricles, additional, Macário, Valmir, additional, Isotani, Seiji, additional, Cordeiro, Thiago, additional, Bittencourt, Ig Ibert, additional, Dermeval, Diego, additional, Gašević, Dragan, additional, and Mello, Rafael Ferreira, additional

Published
2023
Full Text
View/download PDF

128. Enabling Systematic Adoption of Learning Analytics through a Policy Framework

Author

Tsai, Yi-Shan, Scheffel, Maren, Gašević, Dragan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pammer-Schindler, Viktoria, editor, Pérez-Sanagustín, Mar, editor, Drachsler, Hendrik, editor, Elferink, Raymond, editor, and Scheffel, Maren, editor

Published
2018
Full Text
View/download PDF

129. Detecting Learning Strategies Through Process Mining

Author

Saint, John, Gašević, Dragan, Pardo, Abelardo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pammer-Schindler, Viktoria, editor, Pérez-Sanagustín, Mar, editor, Drachsler, Hendrik, editor, Elferink, Raymond, editor, and Scheffel, Maren, editor

Published
2018
Full Text
View/download PDF

130. Automated Analysis of Cognitive Presence in Online Discussions Written in Portuguese

Author

Neto, Valter, Rolim, Vitor, Ferreira, Rafael, Kovanović, Vitomir, Gašević, Dragan, Dueire Lins, Rafael, Lins, Rodrigo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Pammer-Schindler, Viktoria, editor, Pérez-Sanagustín, Mar, editor, Drachsler, Hendrik, editor, Elferink, Raymond, editor, and Scheffel, Maren, editor

Published
2018
Full Text
View/download PDF

131. Towards Combined Network and Text Analytics of Student Discourse in Online Discussions

Author

Ferreira, Rafael, Kovanović, Vitomir, Gašević, Dragan, Rolim, Vitor, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Penstein Rosé, Carolyn, editor, Martínez-Maldonado, Roberto, editor, Hoppe, H. Ulrich, editor, Luckin, Rose, editor, Mavrikis, Manolis, editor, Porayska-Pomsta, Kaska, editor, McLaren, Bruce, editor, and du Boulay, Benedict, editor

Published
2018
Full Text
View/download PDF

138. Practical and ethical challenges of large language models in education: A systematic scoping review.

Author

Yan, Lixiang, Sha, Lele, Zhao, Linxuan, Li, Yuheng, Martinez‐Maldonado, Roberto, Chen, Guanliang, Li, Xinyu, Jin, Yueqiao, and Gašević, Dragan

Subjects
LANGUAGE models, GENERATIVE artificial intelligence, KNOWLEDGE representation (Information theory), CHATGPT, TECHNOLOGICAL innovations, EDUCATIONAL technology, XBRL (Document markup language), NATURAL language processing
Abstract

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks.Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks.Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring.What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation.A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks.Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts.Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks.The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved.Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

139. On the role of politeness in online human–human tutoring.

Author

Lin, Jionghao, Raković, Mladen, Li, Yuheng, Xie, Haoran, Lang, David, Gašević, Dragan, and Chen, Guanliang

Subjects
INTELLIGENT tutoring systems, COURTESY, PROBLEM-based learning, LINGUISTIC politeness, ACADEMIC achievement, TUTORS & tutoring
Abstract

Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed; (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems; (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication; and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. Practitioner notesWhat is already known about this topic Human–human tutoring is acknowledged as an effective instructional method.Polite expression can help strengthen the relationship between tutors and students.Polite expression can promote students' learning achievements in many educational contexts.What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues.Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues.Politeness alone was insufficient to predict the students' performance.Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort.The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress.To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

140. Lessons Learnt from a Multimodal Learning Analytics Deployment In-the-wild

Author

Martinez-Maldonado, Roberto, primary, Echeverria, Vanessa, additional, Fernandez-Nieto, Gloria, additional, Yan, Lixiang, additional, Zhao, Linxuan, additional, Alfredo, Riordan, additional, Li, Xinyu, additional, Dix, Samantha, additional, Jaggard, Hollie, additional, Wotherspoon, Rosie, additional, Osborne, Abra, additional, Shum, Simon Buckingham, additional, and Gašević, Dragan, additional

Published
2023
Full Text
View/download PDF

144. Analytics of self-regulated learning scaffolding: effects on learning processes

Author

Li, Tongguang, primary, Fan, Yizhou, additional, Tan, Yuanru, additional, Wang, Yeyu, additional, Singh, Shaveen, additional, Li, Xinyu, additional, Raković, Mladen, additional, van der Graaf, Joep, additional, Lim, Lyn, additional, Yang, Binrui, additional, Molenaar, Inge, additional, Bannert, Maria, additional, Moore, Johanna, additional, Swiecki, Zachari, additional, Tsai, Yi-Shan, additional, Shaffer, David Williamson, additional, and Gašević, Dragan, additional

Published
2023
Full Text
View/download PDF

149. Towards Automatic Boundary Detection for Human-AI Hybrid Essay in Education

Author

Zeng, Zijie, Sha, Lele, Li, Yuheng, Yang, Kaixun, Gašević, Dragan, and Chen, Guanliang

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Human-AI collaborative writing has been greatly facilitated with the help of modern large language models (LLM), e.g., ChatGPT. While admitting the convenience brought by technology advancement, educators also have concerns that students might leverage LLM to partially complete their writing assignment and pass off the human-AI hybrid text as their original work. Driven by such concerns, in this study, we investigated the automatic detection of Human-AI hybrid text in education, where we formalized the hybrid text detection as a boundary detection problem, i.e., identifying the transition points between human-written content and AI-generated content. We constructed a hybrid essay dataset by partially removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) Separated AI-generated content from human-written content during the embedding learning process; and (2) Calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two prototypes that have the furthest distance from each other. Through extensive experiments, we summarized the following main findings: (1) The proposed approach consistently outperformed the baseline methods across different experiment settings; (2) The embedding learning process (i.e., step 1) can significantly boost the performance of the proposed approach; (3) When detecting boundaries for single-boundary hybrid essays, the performance of the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a $22$\% improvement (against the second-best baseline method) in the in-domain setting and an $18$\% improvement in the out-of-domain setting., Comment: 9 pages including references, 2 figures

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results