An Overview of Selected Learning Theories about Student Learning


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  • NAIT developing its Learning Outcomes Guide (LOG) system which uses 222 verbs in three domains and three levels in each domains.
  • An Overview of Selected Learning Theories about Student Learning

    1. 1. An Overview of Selected Learning Theories about Student Learning Sanjay Goel Jaypee Institute of Information Technology, Noida, India [email_address] , [email_address]
    2. 2. Computing Education <ul><li>Weakest contribution of engineering education in computing related disciplines is in (58 professionals) </li></ul><ul><ul><li>Decision making ability 41% </li></ul></ul><ul><ul><li>Thinking ability 24% </li></ul></ul><ul><ul><li>Procedural knowledge 15% </li></ul></ul><ul><ul><li>Conceptual Knowledge 15% </li></ul></ul><ul><ul><li>Learning ability 3% </li></ul></ul>
    3. 3. Mark Guzdia, Education - From Science to Engineering: Exploring the dual nature of computing education research, CACM Feb 2011 1. Soloway’s 1983 study at Yale: “The Rainfall Problem”: Write a program that repeatedly reads in positive integers, until it reads the integer 99999. After seeing 99999, it should print out the average. Only 14% of students CS1 could solve this problem correctly. 2. Hestenes’ 1985 study : 80% students could state Newton’s Third Law at the beginning of the course… <15% of them fully understood it at the end. 3. McCracken’s 2001 MIMN study wrt CS1: WAP to evaluate arithmetic expressions in a text file. The average score of 215 students was 21%. Many of the participants never got past the design part of the problem to write any code at all. 4. Tew’s 2010 PhD thesis 3 universities wrt CS1 : Majority of her 952 test-takers failed both pseudo-code and native language exams , based on a small subset of what anyone teaches in CS1.
    4. 4. National Survey of Student Engagement (NSSE)
    5. 5. Stages of Intellectual Development, W.G. Perry, 1970 <ul><li>Two Central interwoven dynamics: </li></ul><ul><ul><li>Confronting and coping with diversity and multiples: </li></ul></ul><ul><ul><ul><li>Multiple opinions about a given subject or issue (1-3); </li></ul></ul></ul><ul><ul><ul><li>Multiple contexts/perspectives from which to understand or analyze issues or arguments (4 - 6); </li></ul></ul></ul><ul><ul><ul><li>Multiple Commitments through which one defines his or her values and identity (7 - 9). </li></ul></ul></ul><ul><ul><li>Evolution of meaning making about learning and self </li></ul></ul><ul><ul><ul><li>Knowledge is seen as increasingly conjectural and uncertain, open to (and requiring) interpretation </li></ul></ul></ul><ul><ul><ul><li>Role of the student -- moving from a passive receptor of facts to an active agent in defining arguments and creating new knowledge. </li></ul></ul></ul><ul><ul><ul><li>Role of the teacher - - moving from an Authority as the source of &quot;Truth&quot; to an authority as a resource with specific expertise to share. </li></ul></ul></ul>
    6. 6. Stages of Intellectual Development, W.G. Perry, 1970
    7. 7. Perry Levels Perceptions of Knowledge Perceptions of Solutions Perceptions of self Responsibility Perceptions Teacher’s Responsibility Ability to Make Commitments Dualism - concrete thinkers who believe things are right/wrong, we/they, good/bad. Knowledge is a set of truths. There is a single correct solution to every problem. receive explanations of knowledge and become uneasy when asked to think independently, draw conclusions, or give my points of view. Experts are authorities with an ability to explain and give me correct answers. faith in, and a commitment to, truth and knowledge as it is stated by genuine authorities. Multiplicity - recognize that diversity in thinking exists. Uncertainty prevails because all opinions are valid. Knowledge is a matter of educated opinion. There is no one right solution to a problem, because all are equally valid. listen to experts, but have a right to my own opinions. Experts explain course material to me and express their opinions. feel no need to commit to any specific belief or mode of thinking. Relativism - perceive that all knowledge is relative , and that they need to orient themselves based on evidence. Knowledge is not universal , but a matter of context and situation. What is true in one situation may be false in another. Ambiguity is part of life defend own position on problem solutions based on evidence. make comparisons to distinguish between weak and strong evidence in determining knowledge. Based on their experience, experts teach procedures and analytic methods to help me reason and compare alternatives. feel there is a need for some form of personal commitment. Commitment - develop the need to take positions and commit to them. Knowledge is constructed from experience, what is learned from others, and from reflective thinking. There are many solutions to each problem; some are better, and some are worse. Take a stand on issues based on my personal values and analysis.   learn and integrate new knowledge with what I already know. Experts are mentors that challenge my assumptions to support my learning. feel the need to make commitments, especially a personal commitment to learning.
    8. 9. What kind of learning experiences caused the forward movement? <ul><li>Unexpected results. </li></ul><ul><li>Questions regarding evidence and choice. </li></ul><ul><li>Variety of Observation. </li></ul><ul><li>Absence of satisfactory answers from authority. </li></ul><ul><li>Assignment at Bloom’s higher level, application in new context. </li></ul><ul><li>Engagement in Reasoning. </li></ul>
    9. 10. Learning retention rates: Some results <ul><li>5% Lecture </li></ul><ul><li>10% What we read </li></ul><ul><li>15% What we see </li></ul><ul><li>20% Audio-Visual </li></ul><ul><li>20% What we see and hear </li></ul><ul><li>20% What we hear </li></ul><ul><li>26% What we hear </li></ul><ul><li>30% What we see </li></ul><ul><li>30% Passive Verbal </li></ul><ul><li>30% Demonstration </li></ul><ul><li>40% What we discuss </li></ul><ul><li>50% Visual Receiving </li></ul><ul><li>50% See and hear </li></ul><ul><li>50% Discussion Group </li></ul><ul><li>70% Discuss with others </li></ul><ul><li>70% Active Receiving and Participating </li></ul><ul><li>70% Say </li></ul><ul><li>70% Say and Write </li></ul><ul><li>70% Say or Write </li></ul><ul><li>70% Say as they talk </li></ul><ul><li>75% Practice by Doing </li></ul><ul><li>80% Experience Personally </li></ul><ul><li>80% What we experience directly or practice doing </li></ul><ul><li>90% Say as they do a thing </li></ul><ul><li>90% Say and perform a task </li></ul><ul><li>90% Teach to others/Immediate Use </li></ul><ul><li>90% What we attempt to teach others </li></ul><ul><li>95% of what we teach someone else </li></ul>Sources: Bruce Nyland, 1950’s Wiman and Mierhenry, 1960, 1969 Standard Oil of NY Socony-Vacuum Oil Company Dale and Nyland, 1985 Nyland/Dole, 1972 NTL Institute James Stice, 1984 Seminar Gustafson, 1985 Brady, 1989 Glasser, 1990 Bruce Nyland, 2000
    10. 11. Androgogy Knowles, 1970 <ul><li>Learners need to know why they need to learn something. </li></ul><ul><li>Adults need to learn experientially. </li></ul><ul><li>Adults approach learning as problem-solving. </li></ul><ul><li>Adults learn best when the topic is of immediate value. </li></ul>
    11. 12. What working IT engineers think about Teaching Methods?, SPINE based Study, 2004-05 No (j) Teaching Method Normalised Figure of Merit (Max. = 10) Category 1 Group Projects 10.0 Pivotal 2 Project 9.8 Pivotal 3 Practical Training 9.2 Pivotal 4 Industrial Training /Internship 6.5 Obligatory 5 Lecture 6.5 Obligatory 6 Seminars 6.3 Obligatory 7 Written projects/studies 6.2 Obligatory 8 Home work/Out of class assignment 3.8 Complementary
    12. 13. Effective lecturing in engineering and computing courses, 2005-06? <ul><li>Documentation : 250 Anecdotes of most effective lecture </li></ul><ul><ul><li>110 anecdotes of as recalled by computing students </li></ul></ul><ul><ul><li>99 anecdotes of as recalled by faculty from their student days </li></ul></ul><ul><ul><li>43 anecdotes of as recalled by faculty as teachers. </li></ul></ul><ul><li>Observations </li></ul><ul><li>Most effective lectures were found to have at least one form of active and collaborative learning strategies e.g., problem solving, group work, discussions, critique and so on: </li></ul><ul><ul><li>90% anecdotes by final year students </li></ul></ul><ul><ul><li>55% anecdotes by second year students </li></ul></ul><ul><ul><li>80% anecdotes by faculty members (as students) </li></ul></ul><ul><ul><li>94% anecdotes by faculty members (as teachers) </li></ul></ul>
    13. 14. What students think about lectures attributes? Goel Sanjay (2006), Do Engineering Faculty Know What’s Broken? The National Teaching & Learning Forum , Vol 15 Number 2, USA Lecture Format property Most Effective for learning Least Effective for learning Most Often used 1. careful listening and preparing notes 36.36% 70.45% 79.55% 2. explain textbook 11.36% 90.91% 88.64% 6. creative thinking 75.00% 4.55% 9.09% 7. in-class-group-work 63.64% 4.55% 2.27% 14. discover 63.64% 2.27% 0.00% Correlation Most Effective for Learning Least Effective for Learning Least Effective for Learning -0.79 Most Often used Lecture Format -0.69 0.99
    14. 15. Table A9.1: 29 students’ responses on ‘questioning in the class’,2005 <ul><li>Learning is a consequence of thinking, and knowing facts is only small part of it. </li></ul><ul><li>Most students attend classes of most teachers mostly to meet the attendance requirements. </li></ul><ul><li>Only some students take initiative to ask questions when confused or curious, and very few asked questions that required thinking and contribute to classroom discussions. </li></ul><ul><li>Only a few teachers ask sufficient number of questions during lecture classes. </li></ul><ul><li>Only some teachers give sufficient wait-time (at least few seconds) before calling a student to answer their questions during their lectures. </li></ul><ul><li>Most questions asked by most teachers are related to facts, syntax, formula, procedure or recall that do not require deep thinking. Very few teacher questions enhance creative/analytical thinking, or promote teamwork. </li></ul><ul><li>Very few teachers help students to expand their initial answers through more probing conversations or help them through cues and clues. </li></ul>
    15. 16. Table A10.1 : Effectiveness of educational experiences for competency enhancement of software developers 67 Software developers - (How) Did your college help you in your development ?” Pedagogical Engagements Rating Avg (0-4) <ul><li>Projects </li></ul>3.40 <ul><li>Laboratory work </li></ul>2.99 <ul><li>Discussions with other students </li></ul>2.96 <ul><li>Teaching peers/juniors </li></ul>2.84 <ul><li>Thinking and work oriented Lectures </li></ul>2.76 <ul><li>Discussions with Faculty </li></ul>2.70 <ul><li>Industrial Training. </li></ul>2.60 <ul><li>Research Literature survey oriented assignments </li></ul>2.55 <ul><li>Discussions with others </li></ul>2.39 <ul><li>Homework and Tutorial </li></ul>1.97 <ul><li>Knowledge transmission oriented Lectures (explain and follow the textbooks) </li></ul>1.91 <ul><li>Written examinations and required preparation </li></ul>1.85
    16. 17. Table A10.3: Effectiveness of educational experiences for competency enhancement with respect to direct/indirect contribution for final year project by 210 computing students (7th sem) Pedagogical Engagements Rating, 0-4 <ul><li>Minor project-I/Minor project-II of 3rd year </li></ul>2.8 <ul><li>Mini projects as part of specific courses </li></ul>2.8 <ul><li>Laboratory work (during laboratory classes) </li></ul>2.7 <ul><li>Industrial Training </li></ul>2.5 <ul><li>Developmental work (for laboratory classes) </li></ul>2.5 <ul><li>Discussions with faculty </li></ul>2.4 <ul><li>Literature survey oriented assignments </li></ul>2.2 <ul><li>Discussions with peers/seniors </li></ul>2.1 <ul><li>Lectures </li></ul>1.9 <ul><li>Tutorial </li></ul>1.8 <ul><li>Written examination and required preparation </li></ul>1.6 <ul><li>Mentoring juniors </li></ul>1.5
    17. 18. Two Core Principles Related to Learning <ul><ul><ul><li>Cognitive Dissonance Leon Festinger (1957) </li></ul></ul></ul><ul><ul><li>Humans are sensitive to inconsistencies between actions and beliefs. </li></ul></ul><ul><ul><li>Recognition of an inconsistency results in cognitive dissonance , and motivates an individual to resolve the dissonance. </li></ul></ul><ul><ul><li>Dissonance can be resolved in one of three ways: </li></ul></ul><ul><ul><ul><li>change in beliefs, </li></ul></ul></ul><ul><ul><ul><li>change actions, or </li></ul></ul></ul><ul><ul><ul><li>change perception of actions . </li></ul></ul></ul><ul><ul><ul><li>Cognitive Flexibility Rand Spiro (1991) </li></ul></ul></ul><ul><li>The ability to ‘transfer’ what learners have learned in a context, to different, even unique situations is referred to as ‘cognitive flexibility’ </li></ul><ul><ul><li>In advanced knowledge domains, interconnectedness of ideas must be emphasized. </li></ul></ul><ul><ul><li>For deeper learning, Information must be presented in a variety of ways and contexts </li></ul></ul>
    18. 19. Teaching <ul><li>Socrates </li></ul><ul><li>Galileo </li></ul><ul><li>Einstein </li></ul>
    19. 20. Teaching <ul><li>Socrates </li></ul><ul><li>I cannot teach anybody anything, I can only make them think. </li></ul><ul><li>Galileo </li></ul><ul><li>You cannot teach a man anything. </li></ul><ul><li>You can only help him to find it for himself. </li></ul><ul><li>Einstein </li></ul><ul><li>I never teach my pupils; I only attempt to provide the conditions in which they can learn. </li></ul>
    20. 21. Bloom’s Taxonomy: Levels of Cognition <ul><li>Goel Sanjay and Sharda Nalin (2004), What do engineers want? Examining engineering education through Bloom’s taxonomy, Conference of Australasian Association of Engineering Education, September, 2004, Australia. </li></ul><ul><li>Goel Sanjay (2004), What is high about higher education : Examining Engineering Education Through Bloom’s Taxonomy, The National Teaching & Learning Forum, Vol. 13 Number 4, pp 1-5, USA . </li></ul>What students think they get to do? calculate, explain, prove (studied theorem, studied method), define (studied definitions), write, solve, compute, show (studied fact, studied method), evaluate(computation), derive, state, describe, determine, find, analyze, justify, … What students think works well for them wrt learning? design, analyze, understand, build, apply, adapt, implement, create, develop, demonstrate, validate, define (new things), show (unstudied fact in the direct context of studied material) , illustrate, compare, enjoy, correlate, argue, research, evaluate (the options), ... What professional engineers recommend ? analyse, design, develop, implement, evaluate (the options), integrate, build, conclude, define (new things), acquire, demonstrate, justify, assess, organize, formulate, estimate, summarize, categorize, validate, … Correlation What professional engineers recommend ? What students get in exams? -0.57
    21. 22. Rating Comparison     Bloom levels What students think they get ?       What students get in exams ? What students think works well for them ? What engineers recommend ? Knowledge 0.24 0.36 0.04 0.09 Comprehen-sion 0.24 0.16 0.11 0.10 Application 0.22 0.40 0.13 0.10 Analysis 0.14 0.04 0.15 0.19 Synthesis 0.14 0.05 0.46 0.38 Evaluation 0.02 0.00 0.11 0.15
    22. 23. Modifications to Bloom’s Taxonomy <ul><li>Florida Taxonomy of Cognitive Behavior </li></ul><ul><li>(9 levels), 1967 </li></ul><ul><li>Knowledge </li></ul><ul><ul><li>Knowledge of Specifics </li></ul></ul><ul><ul><li>Knowledge of ways and means to deal with specifics </li></ul></ul><ul><ul><li>Knowledge of universals and abstract </li></ul></ul><ul><li>Comprehension </li></ul><ul><ul><li>Translation </li></ul></ul><ul><ul><li>Interpretation </li></ul></ul><ul><ul><ul><li>Compare, summarize, conclude, show cause and effect relationship, give analogy, perform a directed task </li></ul></ul></ul>
    23. 24. Original Terms New Terms <ul><li>Evaluation </li></ul><ul><li>Synthesis </li></ul><ul><li>Analysis </li></ul><ul><li>Application </li></ul><ul><li>Comprehension </li></ul><ul><li>Knowledge </li></ul><ul><li>Creating </li></ul><ul><li>Evaluating </li></ul><ul><li>Analysing </li></ul><ul><li>Applying </li></ul><ul><li>Understanding </li></ul><ul><li>Remembering </li></ul>Anderson & Krathwohl , 2001
    24. 25. Active Engagement Levels: Extending Bloom’s Taxonomy Sanjay Goel, PhD Thesis, 2010 Minger, 2000 Rowe & Boulgerides, 1992 Sternberg, 1999
    25. 26. 1984
    26. 28. Structure of the Observed Learning Outcome (SOLO) Taxonomy, Biggs and Collis, 1982 <ul><li>A framework for understanding that describes thinking processes in a scale of increasing complexity. </li></ul><ul><li>Higher level embraces the previous level but adds something more. </li></ul><ul><li>Quantitative Phase: The amount of detail in response increases. </li></ul><ul><li>Qualitative Phase: The detail in the responses becomes integrated into a structural pattern </li></ul>
    27. 29. SOLO Taxonomy <ul><li>Quantitative Phase: The amount of detail increases. </li></ul><ul><li>Pre-structural – the student acquires bits of unconnected information that have no organisation and make no sense. </li></ul><ul><ul><li>The task is not attacked appropriately and the performance is incompetent. </li></ul></ul><ul><ul><li>Misses the point </li></ul></ul><ul><ul><li>Display incompetence in design . </li></ul></ul><ul><li>Uni-structural – students make simple and obvious connections between pieces of information. Focus on one conceptual issue or naming things. </li></ul><ul><ul><li>One or a few aspects of the task are picked up and used. </li></ul></ul><ul><ul><li>Correct solution of one part of more complex problem. </li></ul></ul><ul><ul><li>Correct answer to simple algorithmic problem requiring substitution of data into formula. </li></ul></ul><ul><ul><li>Identify, Memorise, Do simple procedure </li></ul></ul><ul><ul><li>Weak or simple solutions to solve problem with minimum quality. </li></ul></ul>
    28. 30. SOLO Taxonomy <ul><li>Quantitative Phase: The amount of detail increases. </li></ul><ul><li>Multi-structural – a number of connections are made, but not the meta-connections between them. Indicates understanding of boundaries but not of systems. </li></ul><ul><ul><li>Several aspects of the task are treated as if they were separate. </li></ul></ul><ul><ul><li>Correct solution to multiple part problem requiring substitution of data from one part to the next. </li></ul></ul><ul><ul><li>Enumerate, Classify, Describe, List, Combine, Do algorithms, Explain, interpret </li></ul></ul><ul><ul><li>Design ideas are loosely organized, with different ideas not integrated coherently. </li></ul></ul>
    29. 31. SOLO Taxonomy <ul><li>Qualitative Phase: The detail in the responses becomes integrated into a structural pattern </li></ul><ul><li>Relational – the students sees the significance of how the various pieces of information relate to one another. Indicate orchestration between facts and theory, action and purpose. Understands how to apply the concept to a familiar problem. </li></ul><ul><ul><li>The quantitative aspects become integrated into a coherent whole </li></ul></ul><ul><ul><li>Elegant solution to complex problem requiring identification of variables to be evaluated or hypotheses to be tested. </li></ul></ul><ul><ul><li>Compare/contrast, Explain causes, Integrate, Analyse, Relate, Apply, Summarise, categorise, outline distinguish </li></ul></ul><ul><li>-- Coherent design to satisfy the original needs and specs </li></ul>
    30. 32. SOLO Taxonomy <ul><li>Qualitative Phase: The detail in the responses becomes integrated into a structural pattern </li></ul><ul><li>Extended abstract – at this level students can make connections beyond the scope of the problem or question, to generalise or transfer learning into a new situation. Relates an existing concept or principle in such a way that they are able to handle unseen problems . </li></ul><ul><ul><li>The previous integrated whole may be conceptualized at a higher level of abstraction and generalized to a new topic or area. </li></ul></ul><ul><ul><li>Solution that go beyond anticipated answer for ill-defined problems. </li></ul></ul><ul><ul><li>Create, Theorize, Generalize, Hypothesize, Reflect, Generate, Predict </li></ul></ul><ul><ul><li>Coherent design with new and creative ideas </li></ul></ul>
    31. 34. Learning Objectives VS SOLO level <ul><li>At 2 Danish Universities </li></ul><ul><li>1. All disciplines, 70% courses’ </li></ul><ul><li>aimed for only level 3. </li></ul><ul><li>2. Only a 10% intended objectives </li></ul><ul><li>aimed for level 5 (for some </li></ul><ul><li>disciplines this was <5%). </li></ul><ul><li>3. > 600 science courses (incl. CS) </li></ul><ul><li>2.8 to 3.4 for UG, </li></ul><ul><li>2.9 to 3.8 for PG </li></ul>Claus Brabrand and Bettina Dahl, J. , Using the SOLO taxonomy to analyze competence progression of university science curricula, Journal of of Higher Education, 58, Springer, pp 531–549, February 2009.
    32. 35. Four-dimensional Taxonomy of Pedagogic Engagements in Software Development Education Reflective Engagements Integrative Engagements Active Engagements Collaborative Engagements Individual engagement problem solving activity Inclusion and integration of various ideas and diverse perspectives. Think deeply to evaluate and refine/transform their own approach and views Collaborate with others to solve problems Sanjay Goel, PhD Thesis, 2010