#Contexts for inquiry code#

Impacts and characteristics of computer-based science inquiry learning environments for precollege students. International Society of the Learning Sciences.ĭonnelly, D. Chan (Eds.), Learning 2005: The Next 10 Years! Proceedings of the 2005 Conference on Computer Support for Collaborative Learning (CSCL ’05), May 30–June 4 2005, Taipei, Taiwan (pp. Supporting CSCL with automatic corpus analysis technology. Cambridge, UK: Cambridge University Press.ĭönmez, P., Rosé, C., Stegmann, K., Weinberger, A., & Fischer, F. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. The guided discovery learning principle in multimedia learning.

Biological Sciences Curriculum Study.ĭe Jong, T., & Lazonder, A. The BSCS 5E instructional model: Origins and effectiveness. A., Gardner, A., Van Scotter, P., Powell, J. International Journal of Science Education, 32(3), 349–377. Collaborative inquiry learning: Models, tools, and challenges. īell, T., Urhahne, D., Schanze, S., & Ploetzner, R. Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1–18.

#Contexts for inquiry code#

Our method illustrates how computational models can be trained for specific purposes (e.g., to code IBL phases) with small data sets by using pre-trained models.Īlfieri, L., Brooks, P. We also found that considering the previous and following utterances, as well as the relative position of the utterance, improved the model’s accuracy. The study indicated that the accuracy of the best performing computational model (differentiated attention with pre-trained static embeddings) was slightly better than that of the human coder (58.9% vs. Second, we investigated how the different features of the best performing computational model improved the coding accuracy. Since learners’ needs for support in CSCIL vary in the different inquiry phases (orientation, conceptualization, investigation, conclusion, and discussion), we studied, first, how the coding accuracy of five computational models (based on word embeddings and deep neural networks with attention layers) differed in the various inquiry-based learning (IBL) phases when compared to human coding. We conducted our study in an authentic higher-education physics context where computer-supported collaborative inquiry-based learning (CSCIL) is a popular pedagogical approach. Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Collaboration analytics, computational models, computer-supported collaborative learning, CSCL, CSCIL, deep networks, inquiry-based learning, word embedding Abstract