PPT-Leveraging Examples in e-Learning

Chapter 11 Ken Koedinger 1 Chapter 11 Objectives Identify types of worked examples Design a faded worked example Extending worked examples Add selfexplanation questions Apply multimedia . James G. Shanahan. Independent . Consultant. EMAIL: . James_DOT_Shanahan_AT_gmail.com. July 27, 2011. http://research.microsoft.com/en-us/um/beijing/events/ia2011. /. . [. with . Nedim. . Lipka. , . Tim Dennis – Solution Consultant, QAD. 2. The following is intended to outline QAD’s general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, functional capabilities, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functional capabilities described for QAD’s products remains at the sole discretion of QAD.. Battiti. , Mauro . Brunato. .. The LION Way: Machine Learning . plus.  Intelligent Optimization. .. LIONlab. , University of Trento, Italy, . Apr 2015. http://intelligent-optimization.org/LIONbook. Ken Koedinger. Reading: . KLI paper sections 6-7. 1. Key points. Learning is mostly subconscious/tacit/implicit. Be skeptical of your own intuitions & conscious reflections/beliefs about learning & instruction. in Computer Vision. Adam Coates. Honglak. Lee. Rajat. . Raina. Andrew Y. Ng. Stanford University. Computer Vision is Hard. Introduction. One reason for difficulty: small datasets.. Common Dataset Sizes. Heng. . Ji. jih@rpi.edu. 04/08, 2016. Why is learning important?. So far we have assumed . we know how the world works. Rules of queens puzzle. Rules of chess. Knowledge base of logical facts. Actions’ preconditions and effects. in Computer Vision. Adam Coates. Honglak. Lee. Rajat. . Raina. Andrew Y. Ng. Stanford University. Computer Vision is Hard. Introduction. One reason for difficulty: small datasets.. Common Dataset Sizes. scikit. -learn. http://scikit-learn.org/stable/. scikit. -learn. Machine Learning in Python. Simple . and efficient tools for data mining and data analysis. Built . on . NumPy. , . SciPy. , and . matplotlib. Matthew Veroff, LCSW. CAPTASA Conference. January . 30, 2010. Airplane Story. Journey to assist ailing mother. Big rude passenger to right. Small really rude passenger on my head. What would you do…... Exam 2. Take-home due Tuesday. Read chapter 22 for next Tuesday. Program . 4. Any questions?. Hypothesis Space in . Decision Tree Induction. Conducts a search of the space of decision trees which can represent all possible discrete functions. . Finding Where We Fit. Can never forget the reason for our work and zeal. Mt. 28:19-20; Mk. 16:15-16. . Jn. 3:16-17; 2 Pet. 3:9; . Lk. . 15:7, 10. Leveraging Our Strengths. Finding Where We Fit. We all are given talents that we are expected to use. , please share with a neighbor your earliest learning memory. Framing. Content. Application. Examples. Research . Data. Practice. INTER-DISCIPLINARY. Learning . s. cience disciplines (McGraw-Hill, 2019). Partnerships Business Solutions Leadership -Breakthrough ResultsRDD ASSOCIATES Perishables Expertly MerchandisedRDD Associate Learning AcademyE On the . a. nticipation guide provided, mark whether you agree or disagree with each statement.. What big picture goals do you have for your students?. What is Scaffolding?. Instructional strategy that helps students connect prior knowledge and experience with new information.