Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Lai, C, Wang, Q and Lei, J (2012) What factors predict undergraduate students' use of technology for learning? A case from Hong Kong , Computers & Education, 59(2), pp. 569-579

	Laird, J.E., Rosenbloom, P.S. and Newell, A. (1986) Chunking in Soar: The Anatomy of a General Learning Mechanism , Machine learning 1 pp. 11-46

	Laird. J.E., Rosenbloom, P.S. and Newell, A. (1996) Chunking in SOAR: The Anatomy of a General Learning Mechanism , Machine Learning, no. 1: 11-46

	Lamb, A. (2011) Reading Redefined for a Transmedia Universe , Learning & Leading with Technology, Vol.39(3), p.12-17

	Lamb, R., Akmal, T., & Petrie, K. (2015) Development of a cognition-priming model describing learning in a STEM classroom , Journal of Research in Science Teaching, 52(3), 410–437. https://doi.org/10.1002/tea.21200

	Lamb, R., Akmal, T., & Petrie, K. (2015) Development of a cognition-priming model describing learning in a STEM classroom , Journal of Research in Science Teaching, 52(3), 410-437. https://doi.org/10.1002/tea.21200

	LANE, D. R. (1994) Computer-Mediated Communication in the Classroom: Asset or Liability? , Interconnect'94 Teaching, Learning & Technology Conference. OSU-Okmulgee, Oklahoma, USA

	Langer, E. (2000) Mindful learning , Current Directions in Psychological Science, 9, 220–223

	Langley, Pat (1996) Elements of Machine Learning , San Francisco: Morgan Kaufmann Publishers, Inc.

	Lankshear & Snyder. (2000) Teachers and Technoliteracy: Managing Literacy, Technology and Learning in Schools , Allen & Unwin. St. Leonards, NSW, Australie

	Laptev, D., Buhmann, J.M., (2014) Convolutional decision trees for feature learning and segmentation , Lect. Notes Comput. Sci

	Lasaulce, S (2011) Game Theory and learning for wireless networks , Elsevier, Germany

	Latour, B. (1987) Learning Machine Learning as an Architect, How to? - Presenting and Evaluating a Grasshopper Based Platform to Teach Architecture Students Machine Learning , Harvard university press

	Laura Turnbull, Marc-Thorsten Hütt, Andreas A. Ioannides, Stuart Kininmonth, Ronald Poeppl, Klement Tockner, Louise J. Bracken, et al (2018) Connectivity and Complex Systems: Learning from a Multi-Disciplinary Perspective , Applied Network Science 3 (1): 11

	Laurell, C., Sandström, C., Berthold, A. & Larsson, D. (2019) Exploring barriers to adoption of Virtual Reality through Social Media Analytics and Machine Learning–An assessment of technology, network, price and trialability , Journal of Business Research, 100, 469-474. https://doi.org/10.1016/j.jbusres.2019.01.017

	Laurer, M., Van Atteveldt, W., Casas, A., & Welbers, K. (2023) Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT-NLI , Political Analysis. https://doi.org/10.1017/pan.2023.20

	Lave, J and Wenger, E (1991) Situated Learning: Legitimate Peripheral Participation , Cambridge University Press

	Lave, J and Wenger, E (1991) Situated Learning: Legitimate Peripheral Participation , Cambridge University Press.

	Lave, J (1991) Situating learning in communities of practice , Resnick, LB, Levine, JM and Teasley, SD (eds), Perspectives on Socially Shared Cognition, American Psychological Association, pp. 63-82

	Lave, J. & Wenger, E. (1991) Situated learning: Legitimate peripheral participation (24th ed , Cambridge: Cambridge University Press

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