SELF-REGULATED LEARNING FROM MULTIPLE · PDF fileDomain-specific prior knowledge, ... central...

SELF-REGULATED LEARNING FROM MULTIPLE REPRESENTATIONS Using eye-tracking to uncover and support students’ processing in online learning environments

York, NIRE Seminar

March 17, 2016 MULTIPLE REPRESENTATIONS

KATHARINA SCHEITER | NIRE SEMINAR | MARCH 2016 2

ACTIVE PROCESSING

KATHARINA SCHEITER | NIRE SEMINAR | MARCH 2016

Cognitive Theory of Multimedia Learning (Mayer, 2009) Assumption: Learners need to actively process learning materials

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SELF-REGULATED LEARNING


Boekaerts (1999) 4

SELF-REGULATED LEARNING

WWW&H-rule (Veenman, van Hout-Walters & Afflerbach, 2006)

•  What declarative strategy knowledge •  When & Why conditional strategy knowledge •  How procedural strategy knowledge

! Do students possess this knowledge when learning with multiple representations?

Challenge: How to assess strategies?


ASSESSMENT OF COGNITIVE STRATEGIES

•  intraindividual processing behavior

•  covert cognitive processes

•  prospective and retrospective self-reports have little predictive value ! concurrent assessments recommended (Cromley & Azevedo, 2006; Veenman, 2005)

•  eye tracking as concurrent, non-intrusive assessment


WHAT IS EYE TRACKING?

WHAT IS EYE TRACKING?

•  Recording the position of the eye and tracking its movement over time

•  Provides information on where a person is looking at a screen and for how long


RELEVANT EYE TRACKING EVENTS

•  Fixations •  eye is relatively motionless, located at more or less

the same position for a longer period of time •  position: x,y coordinate plus tolerance zone (50 pixel) •  time: eye remains at this position for at least 100 msec (e.g.,

Rayner, 1998) – the minimum time required for information intake

•  Saccades

•  very fast movements of the eye, ballistic •  no information intake (saccadic suppression)


INTERPRETATION OF EYE MOVEMENTS

•  Eye movements reflect visual attention distribution •  saccades / fixation frequency reflect visual search •  fixation location = focus of attention

•  Eye movements reflect cognitive processing (Just & Carpenter, 1980) •  eye-mind assumption (fixation position = what is

being processed) •  immediacy assumption (fixation duration = how long

something is being processed) •  scanpath = sequence of information processing



Science 09 Dec 2011: Vol. 334, Issue 6061, pp. 1328

FUNCTIONS OF EYE TRACKING IN MULTIMEDIA RESEARCH •  study how people learn from multimedia

materials and describe underlying processes

•  find explanations for why individual differences in learning outcomes occur

•  test assumptions on how instructional design manipulations cause better learning

•  augment other types of assessment (cued retrospective reporting)

•  allow for an online adaption of instruction

•  serve as instructional support (eye movement modelling)


research tool

instructional tool

Scheiter & Eitel (in press)

12









research tool

instructional tool


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USING EYE TRACKING TO ASSESS COGNITIVE LEARNING STRATEGIES

INADEQUATE PROCESSING OF MULTIPLE REPRESENTATIONS •  over-reliance on text at the expense of picture

processing (e.g., Hegarty & Just, 1993; Schmidt-Weigand, Kohnert, & Glowalla, 2010)

•  few attempts to integrate information from text and

pictures (e.g., Mason, Tornatora, & Pluchino, 2013; Scheiter & Eitel, 2015; Schwonke, Berthold & Renkl, 2009)


MASON, TORNATARA & PLUCHINO (2013)

low integrators (n=7)

high integrators (n=23) -  longer picture inspection during 1st encounter -  more integrative transitions -  longer picture inspection during re-reading -  longer text reading during picture re-inspection ! better learning outcomes


4th graders reading a science text

16

IMPLICATION: COGNITIVE GUIDANCE

•  Goal: improve use of cognitive learning strategies •  Interventions (cf. Renkl & Scheiter, in press)

•  design of multimedia materials (e.g., signaling, Scheiter & Eitel, 2015; spatial contiguity, Johnson & Mayer, 2012)

•  processing instructions aimed at fostering integration (Mason, Pluchino, & Tornatora, 2015; Schwonke et al., 2009; Stalbovs, Scheiter, & Gerjets, 2015)


HOW DO IMPLEMENTATION INTENTIONS FOSTER LEARNING?

IMPLEMENTATION INTENTIONS

•  If-then plans that link situations favorable for goal achievement with effective behavioral responses for attaining a goal (Gollwitzer & Sheeran, 2006)

“If I have read a paragraph, then I will search the picture for the contents referred to in the text.”

•  Help to translate intentions into behavior:

•  conditions in “IF”-part becomes highly accessible •  strong link to action specified in “THEN”-part


RESEARCH QUESTIONS

•  Can implementation intentions support the use of cognitive strategies, thereby improving learning?

•  How should implementation intentions be implemented for supporting the use of cognitive strategies?


STUDY

DVs: Learning outcomes, eye movements

Learner characteristics: Domain-specific prior knowledge, motivation

INSTRUCTION: IMPLEMENTATION INTENTIONS

Multimedia learning phase: Mitosis & meiosis


LEARNING MATERIALS

Illustrated expository text (2049 words, 19 static pictures, 21 pages)

No time limit


POST-TESTS

“During mitosis both daughter cells contain 46 single

chromatid chromosomes.”

Sixty verification items (20 items in each category)

Picture Text Integration

Rating: Incorrect / Correct?

“Each of these cells contains 23 single chromatid

chromosomes.”


INSTRUCTION: IMPLEMENTATION INTENTIONS? Content: What type of strategies should be evoked? •  Text comprehension strategies •  Picture comprehension strategies •  Text-picture integration strategies •  Combination + Integration strategies: Integration prompts are generally more

effective (e.g., Peeck, 1994)

Number: How many implementation intentions? •  most studies: only a single implementation intention •  a larger number of implementation intentions has been shown to

work (Achtziger, Gollwitzer & Sheeran, 2008) + larger number: Possibly more adaptive use of cognitive

strategies


DESIGN

n = 20 n = 20 n = 20

n = 20 n = 20 n = 20

1

3

Text Picture Integration

Strategy Type Evoked

Number of Implementation

Intentions

Control Group: without implementation intentions / strategy information (n = 20) < experimental conditions Combined Group: 3 implementation intentions [1 Text, 1 Picture, 1 Integration] (n = 20) > other experimental conditions

•  160 university students (Age Ø 23.2 years, 128 female) •  Computer-based instruction


COMBINED II

Text: “If I have opened a new page, then I will carefully study the heading first!”

Picture: “If I’m looking at a picture, then I will search for its

central elements with regard to content!” Integration: “If I have read a paragraph, then I will search

the picture for the contents described therein!”


0,5

0,6

0,7

0,8

Control Text Picture Integration Combined

No implementation intention

1 implementation intention

3 implementation intentions

RESULTS – LEARNING OUTCOMES

Mea

n A

ccur

acy

Model: Contrasts p = .01 Residual p = .61

β = .22, p = .01

β = -.14, p = .08


EYE TRACKING DATA

Challenges: A total of 14 eye tracking parameters corresponding to nine strategies, a complex study design Analysis: •  Step 1: Testing of assumptions by comparing conditions

that should have high values on parameters with control group ! only six parameters showed the hyothesized difference to the control group

•  Step 2: Mediation analyses using text-picture integration saccades (relevant conditions against control group)


RSULTS – MEDIATION ANALYSIS

Condition

Transitions (text paragraph –

corresponding picture element)

Mean Accuracy

p = .03 p = .001

n.s.

direct effect

95% CI [-.0402, -.0045]


INSTRUCTIONAL SUPPORT: EYE MOVEMENT MODELING EXAMPLES

EYE MOVEMENT MODELING EXAMPLES (JARODZKA ET AL., 2012, 2013; VAN GOG ET AL., 2009)

•  Social Learning Theory: Learning by observing a model while s/he performs a task (Bandura, 1977; Van Gog & Rummel, 2010)

•  Cognitive apprenticeship (Collins, Brown, & Newman, 1989): Teacher verbalizes cognitive actions thereby externalizing internal processes

•  EMME

1. Record eye movements of successful learner

2. Superimpose eye movements on learning material (video)

3. Replay video to learners


EMPIRICAL FINDINGS

•  EMME improve problem solving (van Gog et al., 2009) and performance in complex classifcation tasks (medicial diagnosis, Jarodzka et al., 2012; classification of biological locomotion: Jarodzka et al., 2013)

•  EMME allow teaching of multimedia learning strategies in primary school children (Mason, Pluchino & Tornatora, 2015) •  better integration of text and pictures •  improved learning outcomes


RESEARCH QUESTIONS

•  Do EMME support learning behavior and outcomes •  in adults with pre-existing strategies •  when teaching more complex strategies?

•  Are effects of EMME moderated by prior knowledge and alleged model expertise ? •  perceptual guidance not sufficient for complex

strategies, cognitive interpretation of attended information requires prior knowledge (Study 1)

•  social learning theory: modeling more effective when model performs within a learner‘s zone of proximal development (Study 2)


Instruction PK assessment

Control

EMME (first 4 pages of

learning material)

Posttest

60-75 minutes

DESIGN AND PROCEDURE


EMME: STRATEGIES Category Process

Selection Initial look at picture

Focus on relevant text and picture elements

Organisation Organize of selected elements

Segment text into meaningful units

Integration Relate text and picture to each other

Final look at picture


EXAMPLE


RESULTS – LEARNING BEHAVIOR (STUDY 1)

Fixation times for picture: EMME > Control, β =.29, p =.044

•  EMME: M = 69548.28 ms •  Control: M = 49823.96 ms

Text-picture saccades: EMME > control, β = .76, p < .001

•  EMME: M = 27.83 •  Control: M = 9.67

(independent of prior knowledge)


RESULTS – FREE RECALL (STUDY 1)

0

10

20

30

40

50

low PK high PK

% c

orre

ct

Control

EMME

β = -.45*

β = .31


RESULTS – VERIFICATION ITEMS (STUDY 1)

0

10

20

30

40

50

60

70

80

90

100

low PK high PK

% c

orre

ct

Control EMME

β = .12

β = .62**


THE ROLE OF MODEL EXPERTISE (STUDY 2)

Successful Model

22 „... observe the eye movements of a learner who used effective learning strategies and who thus performed very well in the knowledge test.“

Neutral Model

25 „... observe the eye movements of a learner who took part in the study earlier.“

Control (no EMME)

25

-


(the same EMME was used in both EMME conditions)

40

RESULTS (STUDY 2)


* * ns

CONCLUSIONS









research tool

instructional tool


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LIMITATIONS AND CHALLENGES

•  it‘s a long way to get there! (first eye tracker bought in 2005, first paper published in 2010)

•  no plug-and-play - requires more background knowledge than suggested by manufacturers (e.g., central fixation bias, peripheral vision)

•  materials and study designs need to be adjusted to accommodate eye tracking (e.g., animations, N = 120 students learning 90 minutes)

•  stats are often more difficult to obtain and analyze because data violate assumptions of standard tests like ANOVA (e.g., not normally distributed, missing data)


REFERENCES

Scheiter, K., & Eitel, A. (in press). The use of eye tracking as a research and instructional tool in multimedia learning. In C. A. Was, F. J. Sansosti, & B. J. Morris (Eds.), Eye-Ttacking technology applications in educational research. Hershey, PA: IGI Global.

Scheiter, K., & Eitel, A. (2015). Signals foster multimedia learning by supporting integration of highlighted text and diagram elements. Learning and Instruction, 36, 11-26.

Scheiter, K., & Van Gog, T. (Eds.). (2009). Using eye tracking in applied research to study and stimulate the processing of information from multi-representational sources. Applied Cognitive Psychology, 23(9).

Stalbovs, K., Scheiter, K. & Gerjets, P. (2015). Implementation intentions during multimedia learning: Using if-then plans to facilitate cognitive processing. Learning and Instruction, 35, 1-15.

Van Gog, T., & Scheiter, K. (Eds.). (2010). Eye tracking as a tool to study and enhance multimedia learning. Learning and Instruction, 20(2).


THANK YOU FOR YOUR ATTENTION!

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