Hasil untuk "Education"

Lawrence W. Green

A. Bishop, K. Clements, C. Keitel et al.

C. E. Basch

J. Rink

J. Ozga

Julia Neuberger, R C Tallis

D. Gillborn, D. Youdell

M. Szasz

Pippa Hall, L. Weaver

J. Wellington, J. Osborne

L. Harasim

J. Loughran

J. V. D. van de Ridder, K. Stokking, W. McGaghie et al.

Elizabeth Adams St. Pierre

J. Arnold

M. Kesim, Yasin Ozarslan

Although the physical world is three-dimensional, mostly we prefer to use two-dimensional media in education. The combination of AR technology with the educational content creates new type of automated applications and acts to enhance the effectiveness and attractiveness of teaching and learning for students in real life scenarios. Augmented Reality is a new medium, combining aspects from ubiquitous computing, tangible computing, and social computing. This medium offers unique affordances, combining physical and virtual worlds, with continuous and implicit user control of the point of view and interactivity. This paper provides an introduction to the technology of augmented reality (AR) and its possibilities for education. Key technologies and methods are discussed within the context of education.

Natalia Revenga-Lozano, Karina E. Avila, Steffen Steinert et al.

Multiple external representations (MERs) and personalized feedback support physics learning, yet evidence on how personalized feedback can effectively integrate MERs remains limited. This question is particularly timely given the emergence of multimodal large language models. We conducted a 16-24 week observational study in high school physics (N=661) using a computer-based platform that provided verification and optional elaborated feedback in verbal, graphical and mathematical forms. Linear mixed-effects models and strategy-cluster analyses (ANCOVA-adjusted comparisons) tested associations between feedback use and post-test performance and moderation by representational competence. Elaborated multirepresentational feedback showed a small but consistent positive association with post-test scores independent of prior knowledge and confidence. Learners adopted distinct representation-selection strategies; among students with lower representational competence, using a diverse set of representations related to higher learning, whereas this advantage diminished as competence increased. These findings motivate adaptive feedback designs and inform intelligent tutoring systems capable of tailoring feedback elaboration and representational format to learner profiles, advancing personalized instruction in physics education.

Adriana Aubert

Umama Dewan, Ashish Hingle, Nora McDonald et al.

The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to wholehearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology.

Julien-Pooya Weihs, Adrien Weihs, Vegard Gjerde et al.

The progression from novice to disciplinary expert is a longstanding area of inquiry in educational research. Studies investigating such progressions have often resorted to participants' self-assessments or other qualitative indicators as a starting point to define experience. But does a participant's estimated experience coincide with metrics derived from their conceptual understanding of a discipline? Using data extracted from over 150 concept maps, we first demonstrate that disciplinary experience is a reliable variable to explain differences in conceptual understanding across a highly diverse learners' population. Through a comparison of unsupervised and semi-supervised models, we then motivate clustering participants into three distinguished experience levels, and support such a classification performed in other studies of educational research. By analysing cluster composition, we also identify discrepancies between the perceived and predicted experience levels of the study participants. Lastly, for studies processing participants data through network analysis, we present insights into statistically significant metrics that can characterise each experience level, and advocate for the use of node-level metrics in such studies.