The future of hands on learning technologies-no pictures

The Future of Hands-on Learning Technologies: Motivation and Learning in Context

Susanna MartinDepartment of Psychology, University of Bath

Danaë Stanton Fraser, Mike Fraser, Dawn Woodgate and David Crellin.

Presentation Overview

• Background• Key Concepts• Investigation One: Malmesbury

• Investigation Two: Gower

• Investigation Three: Ownership and Seams

• Investigation Four: Hands-on Vs Hands-on Technology

• Knowledge Transfer Project: Plug Back into Science

• Investigation Five: Longitudinal Engagement and Reflection

• Summary

Background

• Recent years have seen a move towards hands-on learning in pedagogy.

• Increased availability of mobile data loggers which allow children to explore field sites, collect and evaluate data to gain a sense of the real-world process of scientific research. [Cobcroft 2006].

• The Ambient Wood project [Rogers 2004] demonstrated the value of mobile and embedded technologies for students collecting data ‘in the wild’.

• The Participate project identified qualitative relationships between contextual media and learning [Woodgate 2005]. – Ownership of data– Motivation

• Our interest is in researching the relationships in a more quantitative manner.

Key Concepts

• Data Loggers– Record measurements and store data– Combined with GPS– Additional Sensors

• Seams– Instances of disruption to the process.– Seamful = disjointed process– Seamless = fluid transition

• Context– ‘Context of Doing’

• Information gained from the experience

– ‘Contextual Media’• Additional information provided through media

• Aim:– Understand the role of hands-on technology within the current curriculum.

• Method:– 7 GCSE Students completing their Environmental Science coursework.– Observed in the field and in the class room.

• Findings:– The teachers enjoyed the potential of the data loggers but were restricted by

availability and time.– Students were positive, especially after they were familiar with the equipment.– Students shared their data and collaborated closely.– Some students were unclear about what their data was.

Investigation One: Malmesbury

Investigation Two: Gower

• Aim:– Understand the role of hands-on technology with comparison to older types of

technology.

• Method:– AS Level Students completing their Environmental Science coursework.– Observed in the field.

• Findings:– The students were quick to learn.– The students were motivated to explore the loggers and their functions.– Students appreciated the GPS connection.– Students appreciated the instant feedback of the loggers.

Investigation Three: Ownership and Seams

• Aims– To understand the importance of students remaining connected to

their work in terms of motivation and learning.

• Hypotheses– Motivation will improve for data acquired in context (self-collected) – Understanding will improve for data acquired in context (self-collected) – Ability to answer questions on graphs will improve at post-test when

students have generated graphs themselves (regardless of whether they use software or create graphs by hand, rather than having been given pre-produced graphs).

– Pre-generated graphs will be better understood if students acquired the data themselves.


• Methodology– 46 students (14 girls and

24 boys).– Students experienced

different levels of data interaction during the collection and presentation of the data.

– Pre and Posts tests to assess learning and motivation changes.

Pre TestAll Students

Collection

Interventio

n

SELF STUDENTS1,3,5,7,9,11,13,15,17

PEER STUDENTS2,4,6,8,10,12,14,16,18

Pond1,3,5

Field7,9,11

Construction Site

13,15,17

Sound Class Room2,4,6,8,10,12,14,16,18

Return to main classroomPresentation Intervention

SOFTWARE1,2,7,8,13,14,

MANUAL3,4,9,10,15,16

PREPRODUCED5,6,11,12,17,18

Post TestAll Students


• Collection Intervention– Self students visited :• Pond• Field• Construction Area

– Peer students• Learnt about sound inside

the classroom


• Presentation Intervention:– Pre-Produced


• Presentation Intervention:– Manual


• Presentation Intervention:– Software


• Learning Results: Four tests, two had significant results.

– Ability to read from a graph:• A significant difference within the peer

group.• Students who used the pre-

produced graphs had better post test scores than those who manually produced graphs.

– Ability to draw a graph:• All students showed decrease in scores.

• The self group showed a significant decrease.

• The manually produced group got significantly worse.

Condition Degrees of Freedom F Value P Value

Question One

ability to

read

graphs

Pre-Collection 1,36 0.143 >0.05

Pre-Presentation 2,35 1.100 >0.05

Post-Collection 1,36 .042 >0.05

Post-Presentation 2,35 2.671 >0.05

Peer Collection* 2,16 4.922 <0.05*

Self Collection 2,16 0.350 >0.05

Question Two

Draw

and label a graph

Pre-Collection 1,36 0.016 >0.05

Pre-Presentation 2,35 .466 >0.05

Post-Collection 1,33 .619 >0.05

Post-Presentation 2,32 .520 >0.05

Change All** 1,34 7.432 <0.01**

Change Collection* 1,15 16.96 <0.05*

Change Peer 1,18 .503 >0.05

Change Manual* 1,11 5.923 <0.05*


• Motivation Results: 5 Statements linked to motivation.– Likert Scale

• I enjoy using computers to draw graphs –Non Significant• I think collecting data is a waste of time- Significant• I like working with data I have collected- Significant

– Three Choice Answer• Which set of data did you feel more comfortable working with?-Significant• Which set of data do you feel you can explain better?-Significant


I like working with data I have collected– Students who self collected

showed a positive change in opinion.

Strongly Agree

Agree Neither Agree nor Disagree

Disagree Strongly Disagee

0

10

20

30

40

50

60

70

Pre-Test

Post-Test

I think collecting data is a waste of time

– Students who used pre-produced graphs, changed their view in a positive direction (percentages)

Strongly Agree

Agree Neither Agree nor Disagree

Disagree Strongly Disagee

0

10

20

30Pre-Test

Post-Test


Which set of data did you feel more comfortable with? – 68% of the self students preferred location A (their own

data). – 62% of the peer students felt no difference between

locations A and B.

Which set of data did you feel you could explain better? – 60% of the self students felt they had better understanding

of location A (the location which they visited). – Only 18% of the peer students preferred Location A.

Investigation Four: Hands-on Vs Hands-on Technology

• Aim– To investigate Hands-on vs. Hands-on technology within a

class room environment.

• Hypotheses– That motivation would be more positively affected by the

technology intervention than the traditional.– Student learning will be more positively affected by the

technology intervention than the traditional.– Student confidence in their learning will improve

following intervention regardless of whether their learning scores change.

Investigation Four: Hands-on Vs Hands-on TechnologyMethod

Pre TestStage One

Find your resting pulse rate

Stage TwoThink about the impact of exercise

Stage ThreeDesign and run an experiment

Stage FourReflect upon the experiment

60 Second Scientist

Post Test


• Results - Learning and confidence in learning.– Two questions showed significant responses and one question had a ceiling effect.

• When considering if a described experiment was a fair test there was a significant difference overall. However closer inspection showed that students changed their mind in both directions.

• When reporting the resting heart rate, all students improved in their confidence with regard to their response

QuestionLearning Confidence

Overall Manual Data Logger Overall Manual Data LoggerConceptual Knowledge

1 Ability to assess someone else’s data interpretation 0.317 0.317 1.00 .564 .317 1.00

2 Ability to read specific values from the table 0.46 0.782 .50 0.180 .561 .655

3 Ability to select possible factors 1.00 1.00 1.00 0.705 0.655 1.00

4 Ability to understand participant numbers as a role in fair test .317 1.00 .564 0.564 0.257 0.157

5 Ability to know whether the experiment had been a fair test 0.046* 1.00 0.564 0.317 1.00 0.157

Domain Knowledge

6 Resting heart rate value 0.655 0.157 .564 0.003* .034* .034*

7 Effect of gender on pulse rate 0.470 0.317 .785) 0.206 .317 0.414

8 Data logger accuracy .083 0.317 .157 .527 .083 0.705

9 Effect of exercise NA** NA** NA** .317 .317 1.00

10 Consistency of pulse rate was 0.102 0.18 0.317 1.00 .317 0.414


• Results - Motivation in learning.– Two questions showed significant responses.

• Responses showed that students in the manual group became more positive towards the statement “I like working with data I have collected”

• All students showed a decline in agreement towards “I think data collected using special equipment is more accurate.”

QuestionMotivation

Overall Manual Data Logger11 I like working with data that I have collected. .012* 0.033* .18012 I do not find it useful to do an experiment myself. X X X

13 I enjoy using technology to learn. .429 .180 1.0014 My understanding of an idea is better if I can try it out myself .739 .655 .31715 My understanding of an idea is better if someone tells me about it. 1.00 1.00 1.0016 I think collecting data by hand is a waste of time. .793 .317 .52717 I prefer to do something myself rather than use a computer .951 .589 .41418 Having technology makes my life easier. .527 .317 1.0019 I think data collected using special equipment is more accurate. .037* .317 .05220 If I understand the method then I can explain my results better 0.166 1.00 .132


• Initial Conclusions– There was no clear difference between the two hands on experiences.– The data loggers confused the students as they challenged their

current perceptions. – Any form of hands on experience had a positive learning, and

motivation effect with students becoming more confident in their answers.

• Future Analysis– Consider the relationship between a students accuracy and their

confidence.– Repeat the procedure using a different logger.

Investigation Five: Longitudinal Engagement and Reflection

• Aim– To establish the potential effect of changing the level of interaction

with resources, upon a student in terms of their reflection and engagement with the subject.

• Research Questions– Does generating Context Inclusive (CI) data affect a student’s

motivation, engagement and reflection towards a topic? – Do topics with a greater use of data logging technologies affect the

levels of engagement reported by the students?– Do students do better in end of module tests when they have shown

greater levels of reflection and engagement during the module?

Investigation Five: Longitudinal Engagement and Reflection

• Design– 6 Modules,

• Two Taught normally.• Two taught with cameras added to allow the students to generate their

own contextual media.• Two taught with an increased number of data loggers, to provide ‘context

of doing’, students are also provided with cameras in these lessons.

• Measurement– Comparison is made across module (teacher uses a standardised test).– Comparison with a parallel class.– Comparison with results from previous 6 modules.– Students complete a motivation questionnaire.– Potential to investigate type of photos taken by the students.– Interview with the teacher.

Summary

• Five different investigations with a common theme.

• Results indicate qualitatively that there is a benefit to hands on learning technologies.

• However, experience shows quantitative data can be hard to collect.

• Our mixed method approach is the first step towards providing quantitative understanding of the impact of hands-on technology.

Any Questions?

Knowledge Transfer Project: Plug Back into Science

• This project was developed with three key aims;– To engage with teachers– To share ideas and experiences– To influence policy

• Workshops– Collaborate with teachers– Learn from others

References

• Cobcroft, R., Towers, S., Smith, J. & Bruns, A. (2006). Mobile Learning in Review: Opportunities and Challenges for Learners, Teachers and Institutions. In Proceedings Online Learning and Teaching Conference (pp. 21-30).

• Rogers, Y., Price, S., Fitzpatrick, G., Fleck, R., Harris, E., Smith, H., et al. (2004). Ambient Wood : Designing New Forms of Digital Augmentation for Learning Outdoors. Third International Conference for Interaction Design and Children, 3-10. ACM.

• Woodgate, D. & Fraser, D. (2005). eScience and Education 2005: A Review. JISC Report. jisc.org.uk. Retrieved from http://www.jisc.ac.uk/whatwedo/programmes/programme_eresearch/escience_in_education_review.aspx.