1 Economics Students Approaches to Learning in Different Assessment Regimes Economics Students...

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Economics Students’ Approaches to

Learning in

Different Assessment Regimes

Economics Students’ Approaches to

Learning in

Different Assessment Regimes

Tommy Tang & Tim Robinson

Queensland University of Technology, Australia

Paper presented at the Third International Conference in Developments in Economics and Business Education, Cambridge, UK 1-2 Sept 2005.

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Outline1. Introduction

Input-output approach and its limitations

Biggs’ 3-P Model of Learning

2. The study

3. Findings & Discussions

4. Conclusion

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Introduction

Input-output Approach

Q = f(X1, X2, X3, … )

where Q = Learning output (marks)X1 = AptitudeX2 = AgeX3 = Parental occupation…

Limitations

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Biggs 3-P Model of LearningBiggs 3-P Model of Learning

Student characteristics

Teaching context

Learning approach

Learning outcome

Feedback

Feedback

Presage Process Product

(Biggs 1989, modified)

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“An econometric approach which assesses the impact

of easily measurable independent variables on a

single dependent variable may simply ‘miss’ important

responses to teaching change.”

(Shanahan et al., 1997)

“An econometric approach which assesses the impact

of easily measurable independent variables on a

single dependent variable may simply ‘miss’ important

responses to teaching change.”

(Shanahan et al., 1997)

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Research Objective:Construct instruments to investigate

students’ perceptions of assessment, and learning strategies for assessment

Construction of Instrument:SPQ (Biggs), ASI (Entwistle & Ramsden), MSLQ (Pintrich et al), LAQ (Thomas)Focus groupsStudent surveys

The Study

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Subjects:Students in:

Economics 1 Economics 2Business cycles & economic growth

Sample size Week 1: n = 1146Week 2: n = 648

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Findings & Discussions

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Exploratory Factor Analysis (EFA)

MCQ Essay Assignment Essay Exam

(KMO=0.747) (KMO = 0.805) (KMO = 0.793)

High Low

RW2_MCQ .709

CRIT_MCQ .603

DU2_MCQ .560

INTG_MCQ .528

RHI_MCQ .483

RW1_MCQ .461

FDBK_MCQ .387

RLOW_MCQ .333 .311

MEM3_MCQ .792

MEM1_MCQ .685

MEM2_MCQ .570

MEM4_MCQ .231 .306

DU1_MCQ .222

High Low

RW2_ASS .756

DU2_ASS .649

INTG_ASS .576

CRIT_ASS .568

RW1_ASS .543

FDBK_ASS .525

RHI_ASS .501

RLOW_ASS .379 .341

MEM3_ASS .759

MEM1_ASS .641

MEM2_ASS .515

MEM4_ASS .261 .357

DU1_ASS

High Low

RW2_EX .657

DU2_EX .586

CRIT_EX .545

RW1_EX .513

INTG_EX .497

FDBK_EX .494

RHI_EX .417 .322

MEM3_EX .711

MEM2_EX .706

MEM1_EX .587

RLOW_EX .288 .548

MEM4_EX .236 .347

DU1_EX

Perceptions of Assessment

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Confirmatory Factor Analysis (CFA)

The final model

MCQ Essay Exam

2 /df 2.116 2.424 2.458

CFI 0.963 0.964 0.961

RMSEA 0.044 0.047 0.048

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Learning Strategies for AssessmentUnderstanding

Reproducing Integration

Rote-memorising

U6 .529 R6 .509

U3 .496 U1 .463 U2 .400 U4 .383 S10 .518 S6 .473

S11 .456 S7 .453 S8 .406 U5 .245 .318 -.295R2 .726 R1 .716 R5 .623 R4 .448 R3 .239 S4 .683S3 .594S2 .225 .497S1 .434

KMO = 0.835

EFA

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CFA (2/df = 2.22, CFI = 0.949 and RMSEA = 0.034)

The final model

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CFA – 2nd order factor model(2/df = 2.610, CFI = 0.938, RMSEA = 0.038 )

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Assessment type

Level of intellectual skill

N MeanStd. dev.

MCQLOW 578 3.898 0.7953

HIGH 565 3.063 0.6495

Essay ExamLOW 613 3.736 0.7932

HIGH 600 3.591 0.5613

Essay Assignment

LOW 641 2.601 0.8137

HIGH 630 3.896 0.6012

Perceptions of Assessment

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ApproachesAssessment

typeN Mean Std. Dev.

Understanding

MCQ 570 3.469 0.6489

Essay Assignm 628 3.513 0.6194

Essay Exam 604 3.624 0.5937

Integrating

MCQ 566 3.563 0.7558

Essay Assignm 624 3.721 0.7185

Essay Exam 598 3.709 0.7257

Reproducing

MCQ 570 3.640 0.6921

Essay Assignm 630 3.258 0.7144

Essay Exam 603 3.542 0.7073

Rote-memorising

MCQ 572 3.318 0.7802

Essay Assignm 628 3.071 0.8059

Essay Exam 603 3.263 0.7742

Learning Approaches for Assessment

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LOW_mcq LOW_ex LOW_ass HIGH_mcq HIGH_ex HIGH_ass

LOW_mcq 1

(578)

LOW_ex .472** 1

(560) (613)

LOW_ass .105* .178** 1

(576) (613) (641)

HIGH_mcq

-.063 -.068 .224** 1

(563) (546) (562) (565)

HIGH_ex

.107* .050 .089* .329** 1

(546) (599) (599) (543) (600)

HIGH_ass

.151** .166** -.043 .093* .499** 1

(564) (601) (628) (562) (599) (630)

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

Stability of Perceptions

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ApproachAssessment

Pre-course factors

Conclusion

Implication for Future Research

(Stability)