Nicholas Soltis 1 Karen McNeal 1 Rachel Atkins 2 Lindsay Maudlin 2 1 Auburn University Department of Geosciences 2 NC State University Department of Marine Earth and Atmospheric Science ID: 912645
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Slide1
Understanding Student Engagement while Using an Augmented Reality Sandbox
Nicholas Soltis
1, Karen McNeal1, Rachel Atkins2, Lindsay Maudlin21 Auburn University, Department of Geosciences2 NC State University, Department of Marine, Earth, and Atmospheric Science
1
Slide2Why Engagement?Engagement is “based on the constructivist assumption that learning is influenced by how an individual participates in educationally purposeful activities” (Coates, 2005).The majority of literature on student engagement directly or indirectly is concerned with improving student learning.
Thus, engagement is an essential part of the learning process
2
Slide3Skin BiosensorsMeasures Sympathetic activation-increases with excitement or stressors (physical, emotional, cognitive) (Malmivuo and Plonsey
, 1995).Proxy for engagementThe skin is the only organ that is purely innervated by the sympathetic nervous system
Changes in sympathetic activation can be measured by recording subtle electrical changes across the skin’s surface (Electrodermal Activity ( EDA) or Skin Conductance)EDA is measured in microSiemens (μS)While traditionally done on the palm, the EDA signal is discernable on the wrist (Van Dooren et al., 2012) (Poh et al., 2010).
3
Slide4Engagement and Skin Conductance
4
based on Fredricks, Blumenfeld and Paris (2004, 62-63), drawing on Bloom (1956) and
Malmivuo
and
Plonsey
(1995)
Slide5Hand Sensors and EngagementTraditionally used in medicine and psychology, hand sensors are beginning to see use in educational settings
Recent education applications:Understand stress among autistic students during various social interactions (Goodwin, 2016), (
O’Haire et. al., 2015)Measure engagement during various teaching approaches in an introductory environmental geology course (McNeal et. al., 2014). Additional applications: more rigorous activities such as while painting, using scooters, jumping into a ball pit, and even riding a zip line (Hedman et. al., 2012).5
Slide6The Augmented Reality Sandbox
6
Slide7Research QuestionsHow can Skin Biosensors be used to make sense of engagement during a geoscience lab?How do students interact with an AR sandbox?How does the use of an AR sandbox impact student engagement?
How do different ways of using the AR sandbox affect engagement?What factors contribute to or are predictive of engagement while using an AR sandbox?
7
Slide88
Methods
Skin Conductance
Dynamic Video
Post Test
Interviews
How engaged are students using the sandbox?
How are students using the sandbox?
What factors can predict Engagement
What did students think of the sandbox? What are their suggestions?
Demographics
Spatial Reasoning Skills
1
Topographic Map Skills
2
1 Purdue Spatial Visualization Test Revised
2 Topographic Map Assessment
Slide9ParticipantsLab sections of an introductory Earth System Science course91 students consented to wearing hand sensors and took a post test
37.4% female and 62.6% male.33 of those students agreed to a follow-up interview
42% female and 58% male15.4% reported being geology majors9
Slide10DesignCalculating Engagement
2 minute benchmark while students were completing a traditional paper and pencil lab
Percent change in engagement from the 2 minute benchmark and time using the sandbox was calculatedLab Setup 10
Slide11Results: Unstructured Example
11
Skin Conductance (μS)
Time (S)
Unstructured = lower engagement, relaxation?
Slide12Results: Semi-Structured Example
12
Skin Conductance (μS)
Time (S)
Semi-Structured = Fluctuations as students figured out activity
Slide13Results: Structured Example
13
Skin Conductance (
μ
S)
Time (S
)
Structured = Peaks often corresponded to activities
Slide14Descriptive Statistics- Percent Change in Skin Conductance
Condition at Sandbox
MeanNStandard Deviation
Unstructured
27.6%
34
77.4%
Semi-Structured
44.2%
30
48.9%
Structured
42.5%
27
62.7%
Total
37.5%
91
64.5%
14
Percent Change =
x 100
Slide15How the Sandbox is Used-Interactions with Sandbox and Relationship to Engagement
15
R
2
= .084
p
=.038
Percent Change in Engagement= 67.98 -2.19(SB Interaction) + .02 (SB Interaction.)
2
Slide16How the Sandbox is Used-Interactions Between Students and Relationship to Engagement
16
R
2
= .155
p
=.006
Percent Change in Engagement= -23.65 +8.1 (Social
collab
) - .2 (social
collab
.)
2
+ .001 (social
collab
.)
3
Slide17Spatial Reasoning and Engagement Regression Model
17
Percent change in Engagement= 12.98 + .568 (Spatial Reasoning Score)
2
R
2
= .070
p
=.012
Slide18ConclusionsThere is a significant increase in engagement between the benchmark before using the sandbox and while using the sandbox , regardless of treatment
(p=.002)Though there was a difference in means
between treatments, it was not found to be significantHigh standard deviations reduced the power to detect an effectStructured and Semi-Structured Labs had higher means than the unstructured labsThere is a statistically significant quadratic relationship between spatial reasoning skills and engagement (R2= .070, p=.012).There is a statistically significant cubic relationship between how much students interact socially and engagement (
R
2
= .155,
p
=.006).
There is a statistically significant quadratic relationship between how much students interact collaboratively with the sandbox and engagement (
R
2
= .084,
p=.038).
Interaction time between groups of students and the sandbox is significantly higher during the structured vs. unstructured lab
(p
=.016)
.
18
Slide19RecommendationsFuture Research
Establish a baseline for engagement during a paper and pencil labIncrease 2 minute benchmark period before sandbox interaction
Compare classroom settings to controlled settingsAnalyze a combination of approaches (e.g. A lab containing structured and unstructured elements)Continue to explore connections between sandbox use and spatial reasoning skills For Classroom UseProvide students a clipboard while doing more structured lab activities
Keep a facilitator near the sandbox to assist students
Provide flexible time limits
Try to keep groups at the sandbox no greater than 4 students
Pair free play/ exploration with more structured activities
19
Slide20AcknowledgementsThanks to Dr. Katherine Ryker, Dr. Nicole LaDue, Dr. Shelly Whitmeyer
, Dr. Scott Giorgis for their related work on the AR sandbox and learning outcomesThanks to Dr. Katherine Ryker and Dr. Shelly
Whitmeyer for authoring and sharing the lab materials usedThanks to Auburn University Department of Geosciences Advisory Board for travel fundsThanks to North Carolina State University Department of Marine, Earth, and Atmospheric Sciences for providing a venue for our study 20
Slide21References Bloom, B.S. (ed.) (1956) Taxonomy of Educational Objectives: the Classification of Educational Goals. New York: D McKay & Co, Inc.Coates, H. (2005) The Value of Student Engagement for Higher Education Quality Assurance. Quality in Higher Education. 11 (1), pp. 25–36.
Fredricks, J.A., Blumenfeld
, P.C. and Paris, A.H. (2004) School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research. 74 (1), pp.59–109.Goodwin, M. S. (2016). 28.2 Laboratory and home-based assessment of electrodermal activity in individuals with autism spectrum disorders. Journal of the American Academy Of Child & Adolescent Psychiatry, 55S301-S302. doi:10.1016/j.jaac.2016.07.280Guay, R. (1976). Purdue Spatial Visualization Test. West Lafayette, IN: Purdue Research Foundation.Hedman
, E., Miller, L., Schoen, S., Nielsen, D., Goodwin, M., & Picard, R. W. (2012, September). Measuring autonomic arousal during therapy. In Proc. of Design and Emotion (pp. 11-14).
Jacovina
, M. E.,
Ormand
, C., Shipley, T. F., & Weisberg, S. M. (2014). Topographic Map Assessment [Measurement Instrument]. Retrieved from http://silcton. spatiallearning.org/
index.php
/resources/
testsainstruments
.
Malmivuo, J., & Plonsey
, R. (1995).
Bioelectromagnetism
: Principles and applications of bioelectric and
biomagnetic
fields
. Oxford University Press, USA.
McNeal, K.S., Spray, J.M.,
Mitra
, R., Tipton, J.L. (2014). Measuring student engagement, knowledge, and perceptions of climate change in an introductory environmental geology course.
Journal of Geoscience Education,
62, 655-667
O'Haire
, M. E., McKenzie, S. J., Beck, A. M., & Slaughter, V. (2015). Animals may act as social buffers: Skin conductance arousal in children with autism spectrum disorder in a social context.
Developmental psychobiology
,
57
(5), 584-595.
Poh
, M. Z., Swenson, N. C., & Picard, R. W., (2010). A wearable sensor for unobtrusive, long-term assessment of
electrodermal
activity.
IEEE Transactions on Biomedical Engineering, 57
(5).
Van
Dooren
, M., De
Vries
, J. J., Janssen, J. H. (2012). Emotional sweating across the body: comparing 16 different skin conductance measurements locations
.
Physiology & Behavior, 106(2), 298-304. 21
Slide22ConclusionsThere is a significant increase in engagement between the benchmark before using the sandbox and while using the sandbox,
regardless of treatment (p=.002)
Though there was a difference in means between treatments, it was not found to be significantHigh standard deviations reduced the power to detect an effectStructured and Semi-Structured Labs had higher means than the unstructured labsThere is a statistically significant quadratic relationship between spatial reasoning skills and engagement (R2= .070, p=.012).There is a statistically significant cubic relationship between how much students interact socially and engagement (
R
2
= .155,
p
=.006).
There is a statistically significant quadratic relationship between how much students interact collaboratively in the sandbox and engagement (
R
2
= .084,
p=.038).
Interaction time between groups of students and the sandbox is significantly higher during the structured vs. unstructured lab
(p
=.016)
.
22
Slide23Descriptive Statistics- Interview Data: Rating the Sandbox
Treatment
MeanNStandard Deviation
Unstructured
8.83
6
0.983
Semi-Structured
8.29
14
1.326
Structured
9.15
13
0.987
Total
8.73
33
1.180
23
How would you rank your experience in the AR Sandbox?
Slide24Engagement and Spatial Reasoning and Topographic Map Skills
Spatial Thinking Skills
Mean Percent Change in Engagement
N
Standard Deviation
Low
26.0%
35
47.8%
High
52.3%
33
85.9%
24
Topographic Map Skills
Mean Percent Change in Engagement
N
Standard Deviation
Low
24.7%
23
54.4%
High
55.6%
21
89.6%
Low and High represent the aproximate top and bottom third of scores