Impact of Mobile Tech on Children's Motivation and Learning
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Impact of Mobile Tech on Children's Motivation and Learning
- 1. The Impact of Hand Held Mobile Technologies upon Children’s Motivation and Learning. Susanna Martin Department of Psychology, University of Bath Danaë Stanton Fraser, Mike Fraser, Dawn Woodgate and David Crellin.
- 2. Overview of Presentation • Background • Technology • Hypotheses • Process • Results • Implications • Future
- 3. Background • Engage children-Barton (1997) • Ambient Wood-Rogers et al (2004) • SENSE-Stanton Fraser et al (2005) • Participate-Woodgate et al (2008)
- 4. Technology
- 5. Hypotheses • Motivation will improve for data acquired in context (self collected). • Understanding will improve for data acquired in context (self collected). • Pre-generated graphs will be better understood if students acquired the data themselves.
- 6. Participants Three Schools, 46 Students (8 discounted) – Age 12-14 – 14 Girls, 24 Boys Grouping – Collection • 19 Self • 19 Peer – Presentation • 14 Software • 12 Pre-Produced • 12 Manual
- 7. Process Collection • Self Collect • 3 Sites • Peer Collect • Sound Lesson Production • Location A (Student / Partner Visited) • Location B (Researcher Visited)
- 11. Learning Results Four Tests of Learning – Draw, label, read from and identify data from graphs. 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.
- 12. Motivation Results 5 Statements linked to motivation. – Likert Scale • I enjoy using computers to draw graphs • I think collecting data is a waste of time • I like working with data I have collected – Three Choice Answer • Which set of data did you feel more comfortable working with? • Which set of data do you feel you can explain better?
- 13. Motivation Results I think collecting data is a waste of time – Students who used pre- produced graphs, changed their view in a positive direction 0 5 10 15 20 25 30 35 Strongly Agree Agree Neither Agree nor Disagree Disagree Strongly Disagee Pre-Test Post-Test I like working with data I have collected – Students who self collected showed a positive change in opinion. 0 10 20 30 40 50 60 70 Strongly Agree Agree Neither Agree nor Disagree Disagree Strongly Disagee Pre-Test Post-Test
- 14. Motivation Results 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
- 15. Results-Hypotheses Motivation will improve for data acquired in context (self collected). – Confirmed-Students who self collect indicate they are more comfortable and confident with data they have collected themselves. Understanding will improve for data acquired in context (self collected). – Not Supported-The self group performed worse at post test. Pre-Generated graphs will be better understood if students acquired the data themselves. – Reverse- Peer students showed a better post test score when they used pre-generated graphs
- 16. Implications Ownership of data – Has an impact on how students feel about data. Interpretation – Students may reflect differently dependent on how the data is presented Assessment – Allowing students to qualify answers provides an insight into the students reasoning process.
- 17. Future Focus on the role of context – Investigate further self collection but also the idea of media contexts (photos, videos, text) to aid a students concept of the data. Longitudinal – To provide a greater intervention period and to reduce the possibility of novelty effects.
- 18. References Barton, R. (1997) Does data logging change the nature of children’s thinking in experimental work in science? In B. Somekh & N. Davis (eds.) Using IT effectively in teaching and learning: studies in pre-service and in-service teacher education. London: Routledge. ISBN 0-415- 12131-0 Barton, R. (1998) Why do we ask pupils to plot graphs? Physics Education 33, 6, 366-367. Rogers, Y., Price, S., Fitzpatrick, G., Fleck, R., Harris, E., Smith, H., Randell, C., Muller, H., O'Malley, C., Stanton, D., Thompson,M. and Weal, M. (2004). Ambient Wood: Designing new forms of digital augmentation for learning outdoors. In Proc. Interaction Design and Children. pp. 3-10. Stanton Fraser, D., Smith, H., Tallyn, E., Kirk, D., Benford, S., Rowland, D., et al. (2005). The SENSE project: a context-inclusive approach to studying environmental science within and across schools. In Proc. Computer support for collaborative learning. Taiwan. pp. 155-159, May 2005. Woodgate, D., Fraser, D., Paxton, M., Crellin, D., Woolard, A., Dillon, T., et al. (2008). Bringing School Science to Life: Personalization, Contextualization and Reflection of Self-Collected Data. Fifth IEEE International Conference on Wireless, Mobile, and Ubiquitous Technology in Education (wmute 2008), 100-104. Ieee. doi: 10.1109/WMUTE.2008.35.
- 19. Question One-Learning Quantitative Which location was the quietest- a, b or c? Marked out of 1 B) What was the sound level at each location when the time was 10s? Marked out of 6, 2 marks for each correct, one mark if out by +/- 2 Qualitative C and D) Data was lost for 14s for location A please consider whether: one place a range or places should not be replaced E) Please explain your choice for question C Within the PEER group, students who received pre-produced graphs did significantly better in the post-test than those who manually produced.
- 20. Question Two-Learning Please plot the data points from the table and draw a line of best fit on the graph This question is marked out of ten What Possible Mark Description Example Mark Break Down Appropriate Scale X Axis 2 Scale with regular intervals Eg, 0,2,4,6,8, Not, 0,2,5,6,10 1 Y Axis Scale with regular intervals Eg, 60,62,64,66 Not 60,63,64,65,68 1 Label X Axis 4 Labelled as Time 1 Labelled with Seconds 1 Y Axis Labelled as Sound 1 Labelled with dB 1 Title 1 Title using Sound and Time “A graph showing how Sound levels change over Time” “A graph showing sound over a 20 seconds” 1 Data Points 3 All points plotted accurately 3 Up to TWO incorrect 2 Up to FIVE incorrect 1 All students show a decline in scores for post-test, with SELF students and MANUALLY-PRODUCED students performing significantly worse.
- 21. Question Three-Learning What Possible Mark Description Example Mark Break Down X-Axis 2 Label appropriately Label with Time 2 Label with just X 1 (nb not in addition to Time) 1 Label with Unit Can be anything appropriate, Seconds, S, minutes, Hours Not cm, dB, pH etc 1 Y-Axis 2 Label appropriately Label with Sound 2 Label with just Y 1 (nb not in addition to Sound) 1 Label with Unit Can be anything appropriate, dB, Decibels, not Seconds, Minutes etc 1 Title 3 Mentions Sound Noise 1 Mentions Time Can be seconds Needs to be correct 1 Full Sentence “This graph shows how sound changes over time” 1 Below is a graph showing how sound levels change over time, measured in decibels (dB). Please label the axis and give the graph an appropriate title. This question is marked out of nine No Significant results
- 22. Question Four-Learning “James needs to label his graph with each location but he has forgotten which graph is which, using your understanding of sound can you work out which graph fits which location best?” Connecting Graph to Location One mark for each correct Then for each of the three explanations One mark for describing the line (loud, quiet etc) One mark for describing the place (loud, quiet, consistent, etc) One mark for connecting the place and the line Total possible marks is 12 No Significant results
- 23. Motivation Results Which set of data did you feel more comfortable with? Self Student: “Location A- Because this was the one I tested and it took less time to draw a graph because I understood the data better” Peer Student: “No Difference-I didn't go and find any data so it doesn't really matter to me which one I worked with”.
- 24. Motivation Results Which set of data did you feel you could explain better? Self Student: “Location A-Because with this one I know why the data was varied, however I couldn't find out why the other set of data was varied” Peer Student: “No Difference-I think I understand each both the same because I didn't go out and collect the data so I was just working with the data I got given and it didn't matter which one I had”.
Editor's Notes
- In 1997 Barton noted how data logging can change the nature and emphasis of science practical work, enabling the students to become more independent. Since then mobile technology has improved and further research has indicated that engaging students through mobile technology can be highly beneficial. More recently work by Rogers et al in their 2004 Ambient Wood project showed that freedom to explore provides students with the opportunities to interpret and reflect upon their discoveries. Work by Stanton-Fraser et al in 2005 explored the idea of a context inclusive approach to learning, whereby the students recorded their experimentation enabling them to reflect upon their own scientific process. By enabling the students to reflect and compare on their methods the students were able to gain a greater understanding of the scientific process. The Participate Project noted how students are keen to take ownership of their data and that they found it highly motivating to collect their own data. A key factor which arose during the background analysis was that the majority of the research used a qualitative approach, for instance in the Ambient Wood project, data was gathered by analysing video recordings of the students. In the SENSE project the researchers used video recordings of the students analysis and discussion sessions to inform their understanding. This led to our decision to investigate motivation and learning using a primarily quantitative design. We were primarily interested in how manipulating the levels of contact student had with data logging technology during the collection and transformation stages of understanding data. With a key question of whether doing it yourself can give a better conceptual grasp of the data and how it links into the real world.
- 3 sites- counterbalanced: pond, field, construction site. Sound Lesson: saw data loggers but didn’t use them. Students were required to either generate or annotate two graphs.
- Ability to read from a graph: might be due to them needing to work harder to read the graphs during the trial, (the self students may have been more familiar with their data as a consequence of collecting it).... thus gaining more practice with this skill. Ability to draw and label a graph: Arguably both of these groups had more work to do. Also fatigue, and incomplete booklets.
- Likert Scales were assessed using Wilcoxon. Three Choice answer assessed using Chi Square 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 think collecting data is a waste of time This may be due to students realising that when you are analysing data you benefit from having the context of collecting the data yourself.
- Both of these indicated that regardless of the actual level of understanding students who self collected believed they could explain their own data better and felt more comfortable in doing so. Suggesting that by collecting your own data you gain a confidence about your ability to understand and interpret it.
- This can be further manipulated by changing how the students interact with the data and the opportunities provided Manual group-had to spend too long plotting their graph, leaving them no chance to reflect-links to Barton’s 2008 finding that student difficulties in plotting their own graphs can reinforce misunderstandings. This highlights how many conventional tests of learning might be a little narrow. Our initial work suggests that this would be interesting to investigate further.