OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en...

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Dirk Tempelaar Studentkenmerken en ICT- ondersteund leren: leerstijlen, doeloriëntaties en academische motivaties

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Transcript of OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en...

Page 1: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Dirk Tempelaar

Studentkenmerken en ICT-ondersteund leren: leerstijlen, doeloriëntaties en academische motivaties

Page 2: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Keuzeprocessen in ‘blended leren’: de student aan zet• Veel internationaal onderzoek naar het ‘scaffolden

van e-leren’: soms zijn digitale leertools dermate complex, dat het leren erin (voor sommige studenten) ondersteund moet worden door persoonlijke tutors. Onderzoeksvraag: voor wie?

• Maar het omgekeerde kan ook gelden: face-to-face leerprocessen zijn complex (student-gericht leren), digitaal leren kan daar een ondersteuning voor zijn.

• Meest brede vraag: als de student kan kiezen in vormen van leren, zoals digitaal vs face-to-face, welke studenten maken welke keuzes?

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Maastricht University School of Business and Economics

Maastricht’s blended learning

• Problem-based learning (adapted from McMaster University): collaborative learning based on social constructivist principles (with support of lecture cycles)

• Adaptive tutorial ALEKS: individual learning, with e-learning environment based on Knowledge Space theory (AI)

• This blended learning environment is our ‘statistical buffet’ for the students. Prime characteristic is that students continually choose from the buffet: it is not a one-time allocation based on student characteristics at the start of the course, but a repeated choice over a 8 weeks course period.

• Data for this study are 6 relative large (800/1000) cohorts of freshmen business / economics.

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Maastricht University School of Business and Economics

Process of PBL

problem

* description of phenomena* prepared by a team of

teachers* directs learning activities

small group discussion

* what do we already know about the problem?

* what do we still need to know about the problem?* using a specific problem

solving technique (7-jump)

self study

*learning resources*integration of knowledge from different disciplines

exchange of information

* did we acquire a better under-

standing of the processes involved in the problem?

Page 5: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

How PBL? Seven-Jump• Step 1: Read: clarify terms and concepts• Step 2: Problem definition• Step 3: Brainstorm• Step 4: Systematic inventory• Step 5: Formulate learning goals• Step 6: Self-study • Step 7: Report and synthesize

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Maastricht University School of Business and Economics

Roles

* Tutor: monitors the process and content* Discussion leader: leads the discussion/ process:

summarises, activates,asks questions

* Secretary: “memory”of the group, takes minutes* Group members: participate and prepare!!!

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Maastricht University School of Business and Economics

Role of adaptive e-tutorial ALEKS in Statistics education

• ALEKS replaces all ‘practicals’• It adapts to the level of mastery of students, and thus takes into

account prior statistics schooling, and in specific: lack of any prior schooling

• Participation is optional, and most strongly advised for students with no prior schooling Stats, and weak prior schooling Math

• Mastery is assessed in three Quizzes that allow students to achieve ‘bonus scores’ for their final exam. Strong students do not need such bonus, but for weaker students, it can be the difference between passing and failing.

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Maastricht University School of Business and Economics

UM solution: Adaptive e-tutorial: ALEKS

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Maastricht University School of Business and Economics

“Ideal” individual learning-path

• Based on outcomes of entry-assessment, a student could be evaluated at any point on the knowledge space of topic X.

• Student A can have a different learning path than Student D to reach point f

• Ideally, the learning materials and teachings methods should adapt to the knowledge/skills of each

individual student.

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Maastricht University School of Business and Economics

ALEKS learning path

• Knowledge State can be described by

• All mastered items

• Outer Fringe (=Ready to learn ) + Inner Fringe (=Most recently learned)

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Maastricht University School of Business and Economics

Sample of an ALEKS assessment item

Page 12: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Partial sample of an ALEKS learning report

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Maastricht University School of Business and Economics

ALEKS: learning pie

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Maastricht University School of Business and Economics

ALEKS: Ready to learn & Log

Page 15: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

ALEKS: Quiz report

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Maastricht University School of Business and Economics

ALEKS: Question

Page 17: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

ALEKS: Explanation

Page 18: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

ALEKS: Question

Page 19: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

ALEKS: Explanation

Page 20: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Student learning characteristics

• Martin’s Student Motivation and Engagement Wheel

Page 21: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Martin’s Student Motivation and Engagement Wheel

Motivation: students’

energy and drive to learn

Engagement: the behavior that reflects this energy and drive

Boosters: thoughts and behaviors that

enhance motivation & engagement

Mufflers (dempers):

constrained or impeded

motivation andengagement

Guzzlers (verzwelgers):

reduced motivation andengagement

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Maastricht University School of Business and Economics

SMS correlationsBooster thoughts:•SB self belief•LF learning focus•VS value schoolBooster behaviors:•PS persistence•PL planning•SM study managMufflers:•UC uncert contr•FA Failure avoid•AN anxietyGuzzlers•DS disengagem•SS self sabotage

Mufflers

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Maastricht University School of Business and Economics

Self-regulated learning: Vermunt’s learning styles (patterns) model

Learning styles composed of four components:Learning Orientations: students’ learning related attitudes and aims:

Personally interested, Certificate directed, Self-test directed, Vocation directed, Ambivalent

Learning Conceptions: beliefs and views on learning: Construction of knowledge, Intake of knowledge, Use of knowledge, Experience Stimulating Education, Cooperative Education

Cognitive Processing Strategies: Critical processing, Relating & Structuring (together: Deep strategies), Analysing, Memorising & Rehearsing (together: Stepwise strategies), Concrete Processing

Metacognitive regulation strategies: Self-Regulation of learning process, Self-regulation of learning content (together: Self-regulation), External Regulation of learning process, External regulation of learning content (together: External regulation), Lack of Regulation

Cognitive Processing Strategies and Metacognitive regulation strategies are hypothesised to distinguish deep learners (deep strategies, self-regulation), stepwise learners (stepwise strategies, external regulation) and undirected learners

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Maastricht University School of Business and Economics

ILS Cognitive Processing Strategies - ‘Cognitieve

verwerkingsstrategien ’• Relateren &• Kritisch verwerkenSamen diepgaand leren

(onderdeel betekenisgerichte leerstijl)

• Memoriseren &• AnalyserenSamen stapsgewijs leren

(onderdeel reproductiegerichte leerstijl)

• ConcretiserenOnderdeel toepassings-

gerichte leerstijl

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Maastricht University School of Business and Economics

ILS Metacognitive Regulation Strategies - ‘Metacognitieve regulatie strategien’

• Zelfsturing leerproces &

• Zelfsturing leerinhoud Samen zelfsturing (onderdeel betekenisgerichte leerstijl)

• Ext. sturing leerproces &• Ext. sturing leerinhoud

Samen externe sturing (onderdeel reproductiegerichte leerstijl)

• StuurloosOnderdeel ongerichte

leerstijl

Page 26: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

ILS Learning orientations / leerorientaties

• Persoonlijk geïnteresseerd (betekenisgerichte leerstijl)

• Certificaat/diploma gericht ( reproductie-gerichte leerstijl)

• Testgericht ( reproductie-gerichte leerstijl)

• Beroepsgericht ( toepassingsgerichte leerstijl)

• Ambivalent ( ongerichte leerstijl)

Page 27: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

ILS Learning conceptions / leerconcepties

• Constructivistisch, opbouwen kennis (betekenisgerichte leerstijl)

• Opnemen van kennis ( reproductie-gerichte leerstijl)

• Gebruik van kennis ( toepassingsgerichte leerstijl)

• Stimulerend onderwijs ( ongerichte leerstijl)

• Cooperatief/ samenwerkend ( ongerichte leerstijl)

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Maastricht University School of Business and Economics

Instruments:Dweck’s model of self-theories:Theories of Intelligence scales:Subscale: entity theory1. You have a certain amount of intelligence, and you can’t really do much to

change it.2. Your intelligence is something about you that you can’t change very much.3. To be honest, you can’t really change how intelligent you are.4. You can learn new things, but you can’t really change your basic intelligence.

Subscale: incremental theory5. No matter who you are, you can significantly change your intelligence level.6. You can always substantially change how intelligent you are.7. No matter how much intelligence you have, you can always change it quite a

bit.8. You can change even your basic intelligence level considerably.

Remark: In most empirical work, Dweck and co-authors do not include a separate entity and incremental subscales, but do regard one bipolar scale, called implicit theory, with the incremental position and the entity position as the opposite poles.

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Maastricht University School of Business and Economics

Dweck’s views on the role of effort in learning

Dweck & Blackwell hypothesize that implicit theories determine how students view effort. In the entity-theory framework, (the need for) effort signals low intelligence, thus effort is viewed as a negative thing. In the incremental-theory framework, effort is the cue to learning, to enlarging one’s intelligence, and thus viewed as a positive thing.

Subscale: Effort as a negative thing, exerting effort means you have a low ability1. When I work hard at my schoolwork, it makes me feel like I’m not very smart.2. It doesn’t matter how hard you work—if you’re not smart, you won’t do well.3. If you’re not good at a subject, working hard won’t make you good at it.4. If a subject is hard for me, it means I probably won’t be able to do really well at it.5. If you’re not doing well at something, it’s better to try something easier.Subscale: Effort as a positive thing, exerting effort activates your ability6. When I work hard at my schoolwork, it makes me feel I am learning a lot.7. When something is hard, it just makes me want to work more on it, not less.8. If you don’t work hard and put in a lot of effort, you probably won’t do well.9. The harder you work at something, the better you will be at it.10.If an assignment is hard, it means I’ll probably learn a lot doing it.

Page 30: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Dweck: intelligentie zelftheorien en inspanningsopvattingen

• Zeer beperkte correlaties. Uitzondering: positieve rol voor inspanning

Page 31: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Dweck’s Goal choice: learning goal versus performance goalDweck hypothesizes that (1) implicit theories determine achievement goals, and (2)

this relationship relates to relative, not absolute, measures of learning (mastery) and performance goals. So the suggested scale is again bipolar, pitting learning goals against performance goals. However, the scale does not perform well (included in model as dependent, but not as independent construct).

Alternative tool in Dweck’s work: PALS (revised version), and repeated here: Mastery goal, Performance Approach goal, Performance Avoidance goal

Grant & Dweck (2003) instrument: 4 Performance goals, 2 Learning goalsOutcome performance goals: goal of wanting to do well on a particular taskAbility performance goals: goal of seeking to validate one’s abilityBoth Outcome and Ability goals allow a Normative version (wanting to perform

better than others) and a Non-normative version (absolute standard).Learning goals with & without explicit challenge-mastery component.Total spectrum: Outcome goal, Ability goal, Normative Outcome goal,

Normative Ability goal, Learning goal, Challenge-Mastery goal

Page 32: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

PALS: doelorientaties

• Mastery: om te leren

• Performance Approach: prestatie-motivatie

• Performance Avoidance: Vermijdings-motivatie

Page 33: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Dweck’s outcome performance, ability performance & learning goals

• Outcome goal• Ability goal• Normative Outcome

goal• Normative Ability

goal• Learning goal• Challenge-Mastery

goalNormatief:

vergelijkenderwijsNiet-normatief:

absoluut

Page 34: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Metacognition:AILI: Awareness of Independent Learning Inventory

• Developed by researchers of University of Amsterdam: Elshout-Mohr, Meijer, van Daalen-Kapteijns, and Free University of Brussel: Meeus

• Based on Flavells three component model: knowledge, skills, attitudes.• Balanced design with regard to positively and negatively phrased items.• K: Metacognitive Knowledge

– K1: in the person category– K2: about strategies– K3: about study tasks

• R: Metacognitive Skill:– R1: orientation on one's own functioning in a learning episode– R2: monitoring one's execution of a learning episode– R3: evaluation of one's own functioning in a learning episode

• O: Metacognitive Attitude (sensitivity to feedback):– O1: sensitivity to metacognitive experiences (internal feedback during learning) – O2: sensitivity to external feedback on one's cognitive functioning– O3: curiosity with respect to one's own cognitive functioning and development

Page 35: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Metacognitieve vaardigheden

• K: kennis van• R: vaardig in• O: attitude,

sensitiviteit

Page 36: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Expectancy-value based model for Achievement Motivations (subject Attitudes)

• The SATS model describes the relationships between achievement motivations toward the subject statistics. It originates from the Expectancy-Value model Eccles, Wigfield and co-authors, and is adapted to the statistics domain.

• Expectancy for success:– Competence belief, belief in one’s own ability to perform a task– Perception of task demand, the perceived (lack of) difficulty of the task

demand• Subjective task Value; one component, containing: Attainment values: importance of

doing well on a task, Utility value: usefulness; Costs: spent efforts• Subjective task Affect: Intrinsic value: enjoyment gained from doing the task

– Interest– Effort (planned in ex ante, perceived in ex post version)

Page 37: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Vakattitudes Verwachting*Waarde model (Exp * Value)

• Affectie: waarde

• Cognitieve competentie: verwachting

• Value: waarde, extrinsiek

• Difficulty: verwachting

• Interest: waarde, intrinsiek

• Effort: inzet

Page 38: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Cluster analytic study

Two-step cluster procedure on AleksHours and BBclicks

Cluster 1: top e-ALEKS users, average BB

Cluster 2: average e-ALEKS & BB Cluster 3: low e-ALEKS users, average

BB Cluster 4: average e-ALEKS & top BBCluster 5: average e-ALEKS & low BBCluster 6: independent learners/drop-

outs

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TwoStep Cluster Number

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TwoStep Cluster Number

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Maastricht University School of Business and Economics

Clusters compared on learning profiles

•Cluster 1: high on Stepwise, average on Deep, high on External.•Clusters 3 & 6: low on all.

1 2 3 4 5 6

TwoStep Cluster Number

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Page 40: OWD2010 - 2 - Studentkenmerken en ICT-ondersteunend leren: leerstijlen, doelorientaties en academische motivaties - Dirk Tempelaar

Maastricht University School of Business and Economics

Clusters compared on learning profiles

• Cluster 6 is consistent: average on Affect, high on (no)Difficulty, low on Effort. Mirrored in Cluster 1: low in Affect, low in (no)Difficulty, high in Effort.

1 2 3 4 5 6

TwoStep Cluster Number

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Maastricht University School of Business and Economics

Context afhankelijkheid

• Conclusie 1: onze ‘digitale student’ vertoont kenmerken die deels tegengesteld zijn aan die van de ‘ideale pgo-student’

• Conclusie 2: behoefte aan scaffolding kan dus verschillende richtingen opgaan: niet enkel de tutor die een complex leertool ondersteunt, maar ook de leertool die een complexe tutorgroep aanvult

• Conclusie 3: Blended leren voorziet op flexibele wijze aan persoonlijke ondersteuningbehoefte.