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Promoting Deep Learning “A person with a brain full of knowledge is not a teacher … until he or she can convey that knowledge to another person.”

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Presentation on theme: "Promoting Deep Learning “A person with a brain full of knowledge is not a teacher … until he or she can convey that knowledge to another person.”"— Presentation transcript:

1 Promoting Deep Learning “A person with a brain full of knowledge is not a teacher … until he or she can convey that knowledge to another person.” http://teachpsych.org/resources/e-books/eit2008/eit08-09.pdf http://teachpsych.org/resources/e-books/eit2008/eit08-09.pdf

2 Purpose Discuss ways to promote deep learning, not ways to create deep learners. Deep or surface learning????

3 Deep vs. surface learning How do we promote surface learning? How do we promote deep learning? Deep learning activities Agenda

4 Deep vs. Surface Learning Deep Learning (LT Memory)Surface Learning (ST Memory) Involves analyzing new information, relating this information to prior experiences, and leads to understanding and long-term retention of the new information for use in problem solving in unfamiliar contexts. Involves accepting new information as isolated facts, and leads to short-term retention of the information for use as “cookbook” solutions to problems that are not fully understood. Characteristics Looking for meaningRelying on rote learning Relates previous knowledge to new knowledge New knowledge and previous knowledge are unrelated Focuses on the concepts needed to solve a problem (memorizes for understanding) Focuses on reproducing solutions to similar looking problems (memorizes for regurgitation) Approaches to learning, not attributes of individuals

5 How do we promote surface learning?

6 Promoting Surface Learning Rushing to cover too much material Presenting concepts as unrelated facts Assessing more complex concepts without knowledge of foundational principles Assess rote learning

7 How do we promote deep learning?

8 Promoting Deep Learning Allow time for reflection New material requires linkage of multiple concepts and previous experiences Identify appropriate material for deep learning Teachers model attributes – demonstrate cognitive aspects of solving problems What else must be considered for deep learning?

9 Forming a Long-Term Memory Working Memory Attention Emotion & Motivation Sensation Emotions increase attention Enhanced experiences “This is way cool!” Makes sense Relates to past experiences “Aha, I see it!” Has meaning New learning is relevant “I understand!” Visual Verbal Hands-on

10 Learning Episode and Retention Time in Minutes Degree of Retention 010203040 New Information Practice Down-time Closure Prime-time 1 Prime-time 2 Attention Sensation Emotion & Motivation

11 Teach Others / Immediate Use of Learning Practice by Doing Discussion Group Demonstration Audiovisual Lecture Retention by Instructional Method Reading Verbal Processing Verbal and Visual Processing Doing 5% 10% 20% 30% 50% 75% 90% Retention After 24 hours

12 Deep Learning Activities Bloom’s LevelTermsYour Activities Deep Learning Habits of Mind (Creativity) Increasing Complexity CREATE design compose imagine EVALUATE critique judge appraise ANALYZE compare distinguish examine APPLY practice calculate execute UNDERSTAND explain discuss outline REMEMBER define recall recognize

13 Deep Learning Activities Consider the topic “inference about a population mean” in a beginning stats course. Using the previous chart, how could you address each level in class? For instance…

14 Bloom’s LevelTermsActivities for Lesson Deep Learning Habits of Mind (Creativity) Math Knowledge and Application CREATE design compose imagine EVALUATE critique judge appraise ANALYZE compare distinguish examine APPLY practice calculate execute UNDERSTAND explain discuss outline REMEMBER define recall recognize Find a point estimator and sample variance. Expand from point estimates to an interval of plausible values. Create a confidence interval given the appropriate information. Interpret a confidence interval in terms of the original problem. Address how sample size and levels of confidence impact a confidence interval. Locate a data set of interest and provide insight on the population mean. Deep Learning Activities Create a confidence interval given the appropriate information. Locate a data set of interest and provide insight on the population mean. Find a point estimator and sample variance. Address how sample size and levels of confidence impact a confidence interval. Interpret a confidence interval in terms of the original problem. Expand from point estimates to an interval of plausible values. How do we assess deep learning in activities at each level of thinking?

15 Key Ingredients Relate learning to prior experiences - Topic makes sense Exhibit emotion and motivation - Enhance experiences Ensure relevance - Topic has meaning

16 Questions

17 References Engineering Subject Centre Guide: Learning and Teaching Theory for Engineering Academics www.engsc.uk/er/theory/learning.aspwww.engsc.uk/er/theory/learning.asp K. Crawford and A. Fekete (1997), “What do exam results really measure?” Proceedings of the 2nd Australasian Conference on Computer Science Education: 185-190 “Assessing and Developing Metacognitive Skills” The Teaching Professor. (December 2009) “Why Students Struggle in Math Course” The Teaching Professor. (February 2010) R. Carter (2009). The Human Brain Book. D. Sousa (2006). How the Brain Learns. C. Heath and D. Heath (2008). Made to Stick.


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