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The integration of Artificial Intelligence (AI), particularly Large Language Model (LLM)-based systems, in education has shown promise in enhancing teaching and learning experiences.
Reading images
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Dual coding theory: Retrospect and current status
Paivio, A. (1991) · 1991
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The meanings of hands-on science
Flick, L. B. (1993) · 1993
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Achieving scientific literacy
Bybee, R. W. (1997) · 1997
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Multimedia learning: Are we asking the right questions?
Mayer, R. E. (1997) · 1997
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The Complexity of Chemistry and Implications for Teaching
Gabel, D. (1998) · 1998
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self-monitoring skills in statistics. In D. Schunk, & B. Zimmerman (Eds.), Developing Self-Regulated Learners: From Teaching to Self-Reflective Practice
Lan, W. (1998) · 1998
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Teaching all the languages of science: Words, symbols, images, and actions
Lemke, J. L. (1998) · 1998
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Self-regulation behaviors in underprepared (developmental) and regular admission college students
Ley, K. and Young, D. B. (1998) · 1998
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Learning with diagrams
Henderson, G. (1999) · 1999
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Instructional principles for self-regulation
Ley, K. and Young, D. B. (2001) · 2001
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Language and literacy in science education
Wellington, J. and Osborne, J. (2001) · 2001
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Nine ways to reduce cognitive load in multimedia learning
Mayer, R. E. and Moreno, R. (2003) · 2003
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Construction and interference in learning from multiple representation
Schnotz, W. and Bannert, M. (2003) · 2003
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Beschlüsse der Kultusministerkonferenz: Bildungsstandards im Fach Biologie für den Mittleren Schulabschluss
KMK (2004) · 2004
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Developing students’ ability to ask more and better questions resulting from inquiry-type chemistry laboratories
Hofstein, A., Navon, O., Kipnis, M., and Mamlok-Naaman, R. (2005) · 2005
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Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching
Kirschner, P. A., Sweller, J., and Clark, R. E. (2006) · 2006
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The power of feedback
Hattie, J. and Timperley, H. (2007) · 2007
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Expertise reversal effect and its implications for learner-tailored instruction
Kalyuga, S. (2007) · 2007
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Why minimally guided teaching techniques do not work: A reply to commentaries
Sweller, J., Kirschner, P. A., and Clark, R. E. (2007) · 2007
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Visible Learning
Hattie, J. (2008) · 2008
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Promoting pre-experimental activities in high-school chemistry: Focusing on the role of students’ epistemic questions
Neber, H. and Anton, M. (2008) · 2008
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The Role of Multiple Representations in Learning Science: Enhancing Students’ Conceptual Understanding and Motivation
Treagust, D. F. (2008) · 2008
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When learning the hard way makes learning easy: Building better lab note-taking skills
MacNeil, J. and Falconer, R. (2010) · 2010
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Understanding and addressing the achievement gap through individualized instruction and formative assessment
Yeh, S. S. (2010) · 2010
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Scientific language
Gardner, M. (2012) · 2012
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Framework for K-12 Science Education Practices, Crosscutting Concepts, and Core Ideas
NRC (2012) · 2012
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Authentic learning exercises as a means to influence preservice teachers’ technology integration self-efficacy and intentions to integrate technology
Banas, J. R. and York, C. S. (2014) · 2014
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Process mining techniques for analysing patterns and strategies in students’ self-regulated learning
Bannert, M., Reimann, P., and Sonnenberg, C. (2014) · 2014
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The national curriculum in England: key stages 3 and 4 framework document (2014)
Department for Education (2013) · 2014
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“21st-Century Skills”
McComas, W. F. (2014) · 2014
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Foundations of the learning sciences
Nathan, M. J. and Sawyer, R. K. (2014) · 2014
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Do student perceptions of teaching predict the development of representational competence and biological knowledge?
Nitz, S., Ainsworth, S. E., Nerdel, C., and Prechtl, H. (2014) · 2014
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The Signaling (or Cueing) Principle in Multimedia Learning
van Gog, T. (2014) · 2014
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Common mistakes in the construction of diagrams in biological contexts
von Kotzebue, L., Gerstl, M., and Nerdel, C. (2014) · 2014
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Scaffolding for creative product possibilities in a design-based stem activity
Hathcock, S. J., Dickerson, D. L., Eckhoff, A., and Katsioloudis, P. (2015) · 2015
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Framework for 21st century learning
Kay, K. and Dardis, D. (2016) · 2015
Cited alongside, same era.
Effects of feedback in a computer-based learning environment on students’ learning outcomes: A meta-analysis
Van der Kleij, F. M., Feskens, R. C. W., and Eggen, T. J. H. M. (2015) · 2015
Cited alongside, same era.
Schülerschwierigkeiten beim eigenständigen experimentieren
Kechel, J.-H. (2016) · 2016
Cited alongside, same era.
Signaling text-picture relations in multimedia learning: A comprehensive meta-analysis
Richter, J., Scheiter, K., and Eitel, A. (2016) · 2016
Cited alongside, same era.
Conditions that enable effective feedback
Henderson, M., Phillips, M., Ryan, T., Boud, D., Dawson, P., Molloy, E., and Mahoney, P. (2019) · 2019
Cited alongside, same era.
The effects of pre-training types on cognitive load, collaborative knowledge construction and deep learning in a computer-supported collaborative learning environment
Welcome to the Gemini era
DeepMind, G. (2023) · 2023
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Chatgpt for (finance) research: The bananarama conjecture
Dowling, M. and Lucey, B. (2023) · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., Huang, W., Chebotar, Y., Sermanet, P., Duckworth, D., Levine, S., Vanhoucke, V., Hausman, K., Toussaint, M., Greff, K., Zeng, A., Mordatch, I., and Florence, P. (2023) · 2023
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EU AI Act: first regulation on artificial intelligence
European Union (2023) · 2023
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Mme: A comprehensive evaluation benchmark for multimodal large language models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., et al. (2023) · 2023
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Jung, J., Shin, Y., and Zumbach, J. (2019) · 2019
Cited alongside, same era.
Studying the expertise reversal of the multimedia signaling effect at a process level: evidence from eye tracking
Richter, J. and Scheiter, K. (2019) · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020) · 2020
Cited alongside, same era.
Five trends of education and technology in a sustainable future
Burbules, N. C., Fan, G., and Repp, P. (2020) · 2020
Cited alongside, same era.
Generating medical reports from patient-doctor conversations using sequence-to-sequence models
Enarvi, S., Amoia, M., Del-Agua Teba, M., Delaney, B., Diehl, F., Hahn, S., Harris, K., McGrath, L., Pan, Y., Pinto, J., Rubini, L., Ruiz, M., Singh, G., Stemmer, F., Sun, W., Vozila, P., Lin, T., and Ramamurthy, R. (2020) · 2020
Cited alongside, same era.
What is ai literacy? competencies and design considerations
Long, D. and Magerko, B. (2020) · 2020
Cited alongside, same era.
Multimedia learning
Mayer, R. E. (2021) · 2020
Cited alongside, same era.
Imagine & immerse yourself: Does visuospatial imagery moderate learning in virtual reality?
Hartmann, C., Orli-Idrissi, Y., Pflieger, L. C. J., and Bannert, M. (2023) · 2023
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What do university students know about artificial intelligence? development and validation of an ai literacy test
Hornberger, M., Bewersdorff, A., and Nerdel, C. (2023) · 2023
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Audiogpt: Understanding and generating speech, music, sound, and talking head
Huang, R., Li, M., Yang, D., Shi, J., Chang, X., Ye, Z., Wu, Y., Hong, Z., Huang, J., Liu, J., Ren, Y., Zhao, Z., and Watanabe, S. (2023) · 2023
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Challenges and applications of large language models
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., and McHardy, R. (2023) · 2023
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Physics task development of prospective physics teachers using chatgpt
Küchemann, S., Steinert, S., Revenga, N., Schweinberger, M., Dinc, Y., Avila, K. E., and Kuhn, J. (2023) · 2023
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Ai gender bias, disparities, and fairness: Does training data matter?
Latif, E., Zhai, X., and Liu, L. (2023) · 2023
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Nerif: Gpt-4v for automatic scoring of drawn models
Lee, G.-G. and Zhai, X. (2023) · 2023
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Visual instruction tuning
Liu, H., Li, C., Wu, Q., and Lee, Y. J. (2023) · 2023
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Maaz, M., Rasheed, H., Khan, S., and Khan, F. S. (2023) · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Manakul, P., Liusie, A., and Gales, M. J. (2023) · 2023
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The Future of Education and Skills: Education 2023
OECD (2018) · 2023
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Gpt-4 technical report
OpenAI (2023) · 2023
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Gpt-4v(ision) system card
OpenAI (2023) · 2023
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Collaborating with chatgpt: Considering the implications of generative artificial intelligence for journalism and media education
Pavlik, J. V. (2023) · 2023
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The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data only
Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., and Launay, J. (2023) · 2023
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Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2023) · 2023
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PEER: Empowering Writing with Large Language Models
Seßler, K., Xiang, T., Bogenrieder, L., and Kasneci, E. (2023) · 2023
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Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face
Shen, Y., Song, K., Tan, X., Li, D., Lu, W., and Zhuang, Y. (2023) · 2023
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Pandagpt: One model to instruction-follow them all
Su, Y., Lan, T., Li, H., Xu, J., Wang, Y., and Cai, D. (2023) · 2023
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Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B. (2023) · 2023
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Nationality bias in text generation
Venkit, P. N., Gautam, S., Panchanadikar, R., Wilson, S., et al. (2023) · 2023
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Discovery of a structural class of antibiotics with explainable deep learning
Wong, F., Zheng, E. J., Valeri, J. A., Donghia, N. M., Anahtar, M. N., Omori, S., Li, A., Cubillos-Ruiz, A., Krishnan, A., Jin, W., et al. (2023) · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Ye, Q., Xu, H., Xu, G., Ye, J., Yan, M., Zhou, Y., Wang, J., Hu, A., Shi, P., Shi, Y., Li, C., Xu, Y., Chen, H., Tian, J., Qi, Q., Zhang, J., and Huang, F. (2023) · 2023
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Ai and machine learning for next generation science assessment
Zhai, X. (2023) · 2023
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Ai and formative assessment: The train has left the station
Zhai, X. and Nehm, R. H. (2023) · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E., and Stoica, I. (2023) · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M. (2023) · 2023
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Fine-tuning chatgpt for automatic scoring
Latif, E. and Zhai, X. (2024) · 2024
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The past, present, and future of the cognitive theory of multimedia learning
Mayer, R. E. (2024) · 2024
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Scientific communication and the nature of science
Nielsen, K. H. (2012) · 2086
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