VFT: Reedsville in Fall 2018 vs. Salona in Spring 2019 Report
Jiayan Zhao
May 27, 2019
Demographics
one participant in the Salona VFT group was removed because of missing the post-questionnaire| count | average | Male | Female | Sophomore | Junior | Freshman | Senior | White | Asian | Black/African American | Hispanic | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Reedsville | 25 | 19.76 | 15 | 10 | 10 | 9 | 4 | 2 | 20 | 3 | 1 | 1 |
| count | average | Male | Female | Sophomore | Senior | Freshman | Junior | White | Asian | Black/African American | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Salona | 9 | 20.77778 | 5 | 4 | 5 | 2 | 1 | 1 | 7 | 1 | 1 |
SOD
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 25 3.21 0.67 3.2 3.18 0.59 2.07 4.67 2.6 0.43 -0.63
## se
## X1 0.13
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 3.33 0.85 3.2 3.33 0.99 2.2 4.8 2.6 0.35 -1.39 0.28##
## Welch Two Sample t-test
##
## data: group1.dataframe$value and group2.dataframe$value
## t = -0.35872, df = 11.804, p-value = 0.7261
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7977153 0.5725301
## sample estimates:
## mean of x mean of y
## 3.213333 3.325926Tech Enjoyment
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 3.48 1.08 3.5 3.55 0.74 1 5 4 -0.21 -0.54 0.22
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 3.67 1.09 4 3.67 1.48 2 5 3 -0.12 -1.68 0.36##
## Welch Two Sample t-test
##
## data: group1.dataframe$value and group2.dataframe$value
## t = -0.44119, df = 14.126, p-value = 0.6658
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.093359 0.720026
## sample estimates:
## mean of x mean of y
## 3.480000 3.666667Reedsville. Post vs. Salona. Post
Learning experience
Post:“I learned a lot from the virtual field trip.”
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 2.76 1.13 3 2.71 1.48 1 5 4 0.12 -0.59 0.23
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 4 0.87 4 4 1.48 3 5 2 0 -1.81 0.29##
## Welch Two Sample t-test
##
## data: group1.dataframe$value and group2.dataframe$value
## t = -3.3841, df = 18.468, p-value = 0.003213
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.0084325 -0.4715675
## sample estimates:
## mean of x mean of y
## 2.76 4.00
Factor 1: Field Trip (FT) enjoyment
#Post:“I enjoyed using the virtual field trip.”
“I learned a lot from the virtual field trip.”
“Given the possibility, I would do the virtual field trip again.”
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 3.08 0.98 3 3.08 0.99 1 5 4 -0.01 -0.48 0.2
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 4 0.82 4.33 4 0.99 2.67 5 2.33 -0.27 -1.44 0.27##
## Welch Two Sample t-test
##
## data: group1.dataframe$value and group2.dataframe$value
## t = -2.745, df = 16.894, p-value = 0.01387
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.6274439 -0.2125561
## sample estimates:
## mean of x mean of y
## 3.08 4.00
Factor 2: Spatial Situation Model (SSM)
##“Even now, I could still draw a plan of the spatial environment I observed.”
##“Even now, I could still find my way around the spatial environment.”
##“Even now, I still have a concrete mental image of the spatial environment.”
##“In my mind’s eye, I was able to clearly see the arrangements of the objects presented.”
##“I was able to make a good estimate of the size of the spatial environment.”
##“I was able to make a good estimate of how far apart things were from each other.”
##“I was able to imagine the arrangement of the space very well.”
##“I had a precise idea of the spatial surroundings.”
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 3.44 0.55 3.38 3.43 0.37 2.38 5 2.62 0.48 0.87 0.11
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 3.93 0.63 3.75 3.93 0.56 3.25 5 1.75 0.53 -1.48 0.21##
## Welch Two Sample t-test
##
## data: group1.dataframe$value and group2.dataframe$value
## t = -2.0454, df = 12.625, p-value = 0.06224
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.99996194 0.02885083
## sample estimates:
## mean of x mean of y
## 3.445000 3.930556Factor 3: Simulator Sickness (SS)
5 point-likert scale: 1 = none; 5 = severe
(a) General discomfort
(b) Fatigue
(c) Headache
(d) Eye strain
(e) Difficulty focusing
(f) Salivation increasing
(g) Sweating
(h) Nausea
(i) Difficulty concentrating
(j) Fullness of the Head
(k) Blurred vision
(l) Dizziness with eyes open
(m) Dizziness with eyes closed
(n) Vertigo
(o) Stomach awareness
(p) Burping
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 1.93 0.7 1.69 1.9 0.74 1 3.25 2.25 0.4 -1.26 0.14
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 1.91 0.77 2.38 1.91 0.65 1 2.81 1.81 -0.14 -2.08 0.26##
## Wilcoxon rank sum test with continuity correction
##
## data: total$value by total$condition
## W = 120.5, p-value = 0.7695
## alternative hypothesis: true location shift is not equal to 0Factor 4: system evaluation (SE)
5 point-likert scale: 1 = strongly disagree; 5 = strongly agree
## (a) I found the VR system easy to use.
## (b) I did not need any further help during the virtual field trip.
## (c) The level of control provided by the hand controller was appropriate for the virtual field trip.
## (d) The hand controller was ideal for interacting with the virtual environment.
## (e) There were no glitches in the display
## (f) The display resolution was adequate for the virtual field trip
## (g) I found the visual content of the environment to be of high quality.
## (h) Objects in the virtual environment were very realistic
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 3.59 0.68 3.62 3.59 0.56 2.12 5 2.88 0.04 -0.4 0.14
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 4.42 0.5 4.5 4.42 0.74 3.5 5 1.5 -0.43 -1.2 0.17##
## Welch Two Sample t-test
##
## data: group1.dataframe$value and group2.dataframe$value
## t = -3.8335, df = 19.472, p-value = 0.00108
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.2772783 -0.3760551
## sample estimates:
## mean of x mean of y
## 3.590000 4.416667
Factor 5: Self-location
## (d) I felt as though I was physically present in the environment.
## (e) I felt like I was actually there in the environment.
## (f) I felt like I was part of the environment I observed.
## (g) I felt like the objects surrounded me.
## (i) I had the feeling that I was in the middle of the action rather than merely observing.
## (n) It seemed as though I actually took part in the action.
## (o) It seemed as though my self was present in the environment.
## (p) It was as though my true location had shifted into the environment.
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 25 3.17 0.87 3.29 3.16 0.64 1.57 5 3.43 -0.02 -0.51
## se
## X1 0.17
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 9 3.5 0.94 3.88 3.5 0.93 1.62 4.62 3 -0.64 -0.85
## se
## X1 0.31##
## Welch Two Sample t-test
##
## data: group1.dataframe$value and group2.dataframe$value
## t = -0.93312, df = 13.274, p-value = 0.3674
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.1066078 0.4380363
## sample estimates:
## mean of x mean of y
## 3.165714 3.500000Change of VFT attitude (Before vs. After the VFT)
## I can learn the same amount from a virtual field trip as I can from an actual field trip.
## I would rather visit an actual field site than experiencing a virtual field trip. (reverse coding)
## Virtual field trips can replace actual field trips.
## I would like to see more use of virtual field trips in university teaching.
## I think both virtual field trips and actual field trips can be useful in learning geoscience materials.
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 3.14 0.74 3.2 3.13 0.89 2 4.6 2.6 -0.05 -1.03 0.15
##
## Descriptive statistics by group
## group: Reedsville
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 2.62 0.76 2.6 2.63 0.59 1 4 3 -0.03 -0.64 0.15
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 3.29 0.59 3.2 3.29 0.59 2.4 4.2 1.8 0.2 -1.46 0.2
##
## Descriptive statistics by group
## group: Salona
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 3.67 0.6 3.6 3.67 0.59 3 4.8 1.8 0.48 -1.12 0.2
##
## [1] "ANOVA"
## Model df AIC BIC logLik Test
## baseline 1 4 157.5663 166.3249 -74.78315
## MeasureTimeM 2 5 154.9764 165.9247 -72.48820 1 vs 2
## ConditionM 3 6 150.4093 163.5472 -69.20465 2 vs 3
## MeasureTime_condition 4 7 141.7358 157.0634 -63.86792 3 vs 4
## L.Ratio p-value
## baseline
## MeasureTimeM 4.589911 0.0322
## ConditionM 6.567097 0.0104
## MeasureTime_condition 10.673467 0.0011
##
##
##
## [1] "Post-hoc"
## Value Std.Error DF t-value
## (Intercept) 3.1333333 0.1445134 31 21.6819637
## measure.timemeasure.2 -0.5666667 0.1433513 31 -3.9529942
## conditionSalona 0.1555556 0.2767220 31 0.5621366
## measure.timemeasure.2:conditionSalona 0.9444444 0.2744968 31 3.4406398
## p-value
## (Intercept) 2.709799e-20
## measure.timemeasure.2 4.162229e-04
## conditionSalona 5.780654e-01
## measure.timemeasure.2:conditionSalona 1.679456e-03
##
## [1] "Within effect size"
## [1] "r = 0.578910376892475"
##
## [1] "Between effect size"
## [1] "r = 0.100452026150346"
##
## [1] "Interaction effect size"
## [1] "r = 0.525683810874204"
Correlation matrix
## [1] "Reedsville"
##
## row column cor p
## 1 SOD Tech Enjoyment -0.02056310 9.222803e-01
## 2 SOD Learning Experience 0.05564089 7.916521e-01
## 3 Tech Enjoyment Learning Experience -0.05514498 7.934696e-01
## 4 SOD FT Enjoyment 0.09546271 6.498893e-01
## 5 Tech Enjoyment FT Enjoyment -0.05080147 8.094323e-01
## 6 Learning Experience FT Enjoyment 0.92434241 4.203971e-11
## 7 SOD SSM 0.20068652 3.361025e-01
## 8 Tech Enjoyment SSM 0.08998385 6.688297e-01
## 9 Learning Experience SSM 0.41533860 3.895111e-02
## 10 FT Enjoyment SSM 0.48755208 1.342962e-02
## 11 SOD Simulator Sickness -0.21720143 2.969835e-01
## 12 Tech Enjoyment Simulator Sickness -0.23827420 2.513848e-01
## 13 Learning Experience Simulator Sickness -0.41365875 3.982958e-02
## 14 FT Enjoyment Simulator Sickness -0.56167958 3.480860e-03
## 15 SSM Simulator Sickness -0.35762856 7.922917e-02
## 16 SOD System Evaluation -0.24798088 2.320065e-01
## 17 Tech Enjoyment System Evaluation 0.12182624 5.618379e-01
## 18 Learning Experience System Evaluation 0.31247505 1.283198e-01
## 19 FT Enjoyment System Evaluation 0.37541262 6.441944e-02
## 20 SSM System Evaluation 0.26700682 1.969600e-01
## 21 Simulator Sickness System Evaluation -0.17425043 4.048175e-01
## 22 SOD Self-location 0.04899205 8.161043e-01
## 23 Tech Enjoyment Self-location -0.03735516 8.592924e-01
## 24 Learning Experience Self-location 0.74584466 1.872624e-05
## 25 FT Enjoyment Self-location 0.71869596 5.188846e-05
## 26 SSM Self-location 0.46444189 1.933826e-02
## 27 Simulator Sickness Self-location -0.21488991 3.022802e-01
## 28 System Evaluation Self-location 0.45783410 2.137062e-02
## NULL## [1] "Salona"
##
## row column cor p
## 1 SOD Tech Enjoyment 0.50035670 0.170116880
## 2 SOD Learning Experience 0.24882463 0.518528814
## 3 Tech Enjoyment Learning Experience 0.13245324 0.734082310
## 4 SOD FT Enjoyment 0.11996290 0.758525469
## 5 Tech Enjoyment FT Enjoyment 0.04682929 0.904774322
## 6 Learning Experience FT Enjoyment 0.88388348 0.001565278
## 7 SOD SSM 0.47388720 0.197511927
## 8 Tech Enjoyment SSM 0.35946702 0.342040478
## 9 Learning Experience SSM 0.02856754 0.941842246
## 10 FT Enjoyment SSM 0.15150227 0.697204755
## 11 SOD Simulator Sickness -0.34007724 0.370543806
## 12 Tech Enjoyment Simulator Sickness -0.23083941 0.550122588
## 13 Learning Experience Simulator Sickness 0.01169664 0.976174518
## 14 FT Enjoyment Simulator Sickness 0.06203080 0.874036392
## 15 SSM Simulator Sickness -0.64400735 0.061219482
## 16 SOD System Evaluation -0.70686742 0.033231775
## 17 Tech Enjoyment System Evaluation -0.17206180 0.658011510
## 18 Learning Experience System Evaluation 0.28867513 0.451238872
## 19 FT Enjoyment System Evaluation 0.53582588 0.137037791
## 20 SSM System Evaluation -0.19379837 0.617345911
## 21 Simulator Sickness System Evaluation 0.33765291 0.374184543
## 22 SOD Self-location -0.47954008 0.191467038
## 23 Tech Enjoyment Self-location -0.16023533 0.680475130
## 24 Learning Experience Self-location 0.23042861 0.550852573
## 25 FT Enjoyment Self-location 0.54312545 0.130744292
## 26 SSM Self-location 0.06582779 0.866380040
## 27 Simulator Sickness Self-location 0.22909544 0.553224116
## 28 System Evaluation Self-location 0.88137689 0.001682482
## NULL