Pedagogic Enquiry Presentation - Threshold Concepts in Statistics as a Discipline - Dr Richard...
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Threshold Concepts in QuantsDr Richard DiamondEnquiry Presentation
18th May 2011
Postgraduate Certificate in Learning and Teaching in Higher Education
University College London
Enquiry Aims and Activities
• Disciplinary application of the threshold concepts approach to analyse quants (statistics and finance)
• Identification of threshold concepts and effective learning pathways to navigate the webs of concepts
• Analysis of VLE data (grades and time spent)
• Analysis of student feedback (CATs and informal)
• Reflection on and improvement of techniques for teaching, engagement and support
Threshold Concepts Approach Literature Review
Key References
Cousin (2006), Davies and Mangan (2007), Meyer and Land (2003, 2005), Land et al. (2005) and Meyer et al. (2008)
What Is Threshold Concepts Approach About?
• Recognising and focusing on central concepts in contemporary ‘stuffed’ curriculum
• Linking thinking and practicing through re-working of prior naive or premature understandings (common sense vs. science)
• Dealing with the troublesomeness of knowledge that stems from (a) opening up concepts deemed understood and (b) uncertainty about future understanding
Surface Learning Issues in Economics
• Students acquire formal knowledge of a discipline but seem unable to use it when making sense of experience in work or their everyday lives
• Students struggle with underpinning theory and resort to verbatim learning of isolated aspects of the subject that they are unable to juxtapose
Davies and Mangan (2007:18)
Initial Findings from Assessment Data
Design and Sampling
• Three subsequently delivered modules of 14, 51 and 37 participants• Curriculum made ‘simpler’ from one run to next• Same textbook (Barrow 2009) and MathXL questions• Scores had a range of 1 to 100, presenting categorial and finely ranked data
Grades DistributionHighly discretised data bins give approximate Normal
Where is the mean?10% bins provide more difference between students
Distribution is Multi-ModalFitting to the Normal still leaves a problem with bins
Surface Learners
Deep Learners
0
2
4
6
8
10
12
14
16
18
20
30
40
50
60
70
80
90
100
More
Freq
uenc
y
Bin
Histogram
Distribution is Bi-ModalReflects a transition through troublesome knowledge
Surface Learners
Deep Learners
Amount of time spent indicates problem, not result
Group One28 Sections
Group Two20 sections
Initial Findings from Significant Correlations I
• Total time spent by students had no significant correlation with their results. Time spent per section had negative correlation with the section’s score
• In order to score high overall, the students needed to score high for all chapters (grading emphasised particular chapters)
Initial Findings from Significant Correlations II
• Scores in Chapters 1-3 have significant correlation with subsequent results (procedural thresholds being learned, especially in Chapter I)
• Normal Distribution - Hypothesis Testing - Regression Modelling showed the strong triangle relationship. They are threshold concepts and procedures of Statistics
Threshold Concepts for Knowledge Integration
Example of a conceptual mapNotice multiple dimensions that link concepts
Webs of Threshold Concepts
• Two kinds of maps: local and grand
• Learners should have some basic and threshold concept knowledge before being guided through the mapping
• Maps are drawn in front of and together with learners and questions get answered
Technique Improvement:What has been tried in class
Visualisaiton
• Visual simulations for complicated matters. Examples: Type I and Type II errors in justice system. Thousands of weight combinations to show the Efficient Frontier of a financial portfolio
• Sketches of probability distributions. Example: finding any area under the Normal Distribution curve
Improvisation
• Improvising with different meaningful data sets on the go: downloading data from the Internet, applying a statistical technique then soliciting interpretations and acknowledging or providing the most sensible.
• Example: sorting hamburgers and coffee drinks in percentiles for the amount of fat
Experiments with Assessment
• Giving an integrating document such as a final exam early, during the first workshop
• Exam design: calculation question plus several interpretation questions (could be multiple choice)
• Questions on interpretation aim to check the deeper understanding
Exam Design Pendulum in Quantitative Disciplines
• From solely multiple choice questions to sections of several related questions that require calculation and interpretation
• This was the case in how I developed my own exams (over three years) and exams done by colleagues elsewhere
This only scratched a surface of insight which threshold
concepts approach can offer
Technical Slides
Types of Conceptual ChangeDiscipline: Business Statistics
Type of Change Type of Transformation Examples in Business Statistics
Basic
The concepts that have relevance to everyday
experience.
Calculation skills and ability to identify a
statistical measure
Central tendency and dispersion
Mean vs. median
Standard Deviation
Probability (sources, ways of definition)
Discipline
Acquisition of theoretical perspective--ability to
see the world as a statistician.
Basic concepts are related to the outside world
using discipline threshold concepts (e.g., mean
and standard deviation can be plugged in to
probability distribution in order to describe a
specific situation).
Probability Distribution
Continuous vs. discrete
Hypothesis Testing. Significance
Correlation
Regression
Time Series Data
Index
Procedural
(Same description as in quantitative finance, a
more intense area)
An understanding and mastery of the subject’s
modelling procedures that enable the
construction of discipline-specific models,
arguments and ways of practising.
“Mathematical Transformation” - “Magic of
working through a proof”
Essential mathematical notation (summation with indexes)
Operation with equations. Polynomials
Operations with percentages
Meaning of greek letters
Normal Distribution Tables
Ability to visualise a distribution (use a sketch)
(there could well be other, lower level procedural threshold not covered)