Abstract
This study examined students’ genetics learning in a game-based environment by exploring the connections between the expectancy-value theory of achievement motivation and flow theory. A total of 394 secondary school students were recruited and learned genetics concepts through interacting with a game-based learning environment. We measured their science self-efficacy, science outcome-expectancy beliefs, flow experience, feelings of frustration, and conceptual understanding before and after playing the game, as well as their game satisfaction. Mixed-model ANOVA, correlation tests, and path analysis were run to answer our research questions. Based on the results, we found that the game had a significant impact on students’ conceptual understanding of genetics. We also found an acceptable statistical model of the integration between the two theories. Flow experience and in-game performance significantly impacted students’ posttest scores. Moreover, science outcome-expectancy belief was found to be a significant predictor of students’ flow experiences. In contrast, science self-efficacy and pretest scores were found to be the most significant factors influencing the feeling of frustration during the game. The results have practical implications with regard to the positive role that an adaptive game-based genetics learning environment might play in the science classroom. Findings also underscore the role the teacher should play in establishing productive outcome expectations for students prior to and during gameplay.
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Acknowledgements
We want to acknowledge all cooperating teachers who played a significant role in this study. We also thank Dolly Bounajim for helping with data cleaning. This material is based upon work supported by the United States National Science Foundation under Grant Nos. DRL-1503311. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Modeling Secondary Students’ Genetics Learning in a Game-Based Environment: Integrating the Expectancy-Value Theory of Achievement Motivation and Flow Theory.
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Appendices
Appendix 1
Psychometric properties of all instruments used in this study.
Science self-efficacy
Item | Measure (Scale 100) | Infit MNSQ | Outfit MNSQ | Cronbach’s alpha if item deleted | Person reliability | Item reliability |
|---|---|---|---|---|---|---|
I am sure of myself when I do science | 47.07 | 0.88 | 0.91 | 0.676 | 0.72 | 0.96 |
I know I can do well in science | 39.35 | 0.74 | 0.68 | 0.588 | ||
I can handle most subjects well, but I cannot do a good job with science | 44.55 | 1.39 | 1.36 | 0.737 | ||
I am sure I could do advanced work in science | 50.87 | 1.02 | 0.99 | 0.675 |
Science outcome expectancy
Item | Measure (Scale 100) | Infit MNSQ | Outfit MNSQ | Cronbach’s alpha if item deleted | Person reliability | Item reliability |
|---|---|---|---|---|---|---|
I would consider a career in science | 51.78 | 1.23 | 1.23 | 0.903 | 0.88 | 0.96 |
I expect to use science when I get out of school | 41.56 | 1.08 | 1.02 | 0.897 | ||
Knowing science will help me earn a living | 42.55 | 0.89 | 0.90 | 0.891 | ||
I will need science for my future work | 46.49 | 0.83 | 0.83 | 0.889 | ||
Science will be important to me in my life’s work | 47.69 | 0.87 | 0.86 | 0.894 |
Genetics assessment
Item | Measure (Scale 100) | Infit MNSQ | Outfit MNSQ | Cronbach’s alpha if item deleted | Person Reliability | Item reliability |
|---|---|---|---|---|---|---|
Item1 | 45.97 | 0.84 | 0.68 | 0.866 | 0.83 | 0.98 |
Item2 | 45.64 | 0.90 | 0.80 | 0.867 | ||
Item3 | 38.15 | 0.82 | 0.58 | 0.867 | ||
Item4 | 34.72 | 0.82 | 0.56 | 0.868 | ||
Item5 | 49.15 | 1.03 | 1.04 | 0.869 | ||
Item6 | 45.43 | 1.08 | 1.30 | 0.870 | ||
Item7 | 43.55 | 1.19 | 1.30 | 0.873 | ||
Item8 | 49.09 | 0.75 | 0.63 | 0.863 | ||
Item9 | 45.01 | 0.89 | 0.78 | 0.867 | ||
Item10 | 56.99 | 0.94 | 0.95 | 0.867 | ||
Item11 | 55.34 | 0.86 | 0.83 | 0.866 | ||
Item12 | 55.12 | 0.71 | 0.63 | 0.862 | ||
Item13 | 40.72 | 0.94 | 0.81 | 0.869 | ||
Item14 | 62.32 | 1.05 | 1.15 | 0.868 | ||
Item15 | 45.34 | 0.99 | 1.05 | 0.869 | ||
Item16 | 53.54 | 0.98 | 0.97 | 0.867 | ||
Item17 | 37.99 | 0.93 | 0.65 | 0.869 | ||
Item18 | 43.66 | 1.04 | 1.05 | 0.869 | ||
Item20 | 50.67 | 1.00 | 0.98 | 0.867 | ||
Item22 | 56.85 | 1.26 | 1.45 | 0.872 | ||
Item23 | 45.54 | 1.04 | 1.00 | 0.869 | ||
Item24 | 59.58 | 1.32 | 1.41 | 0.873 | ||
Item25 | 72.66 | 1.07 | 1.40 | 0.871 | ||
Item26 | 58.30 | 1.36 | 1.46 | 0.873 | ||
Item28 | 46.30 | 1.09 | 1.35 | 0.870 |
Game satisfaction
Item | Measure (Scale 100) | Infit MNSQ | Outfit MNSQ | Cronbach’s alpha if item deleted | Person reliability | Item reliability |
|---|---|---|---|---|---|---|
Using Geniventure was worthwhile | 49.22 | 0.94 | 1.05 | 0.817 | 0.71 | 0.55 |
I consider my experience a success | 46.20 | 1.10 | 0.92 | 0.833 | ||
My experience was rewarding | 49.08 | 1.01 | 0.95 | 0.810 | ||
I would recommend Geniventure to my classmates | 50.25 | 0.91 | 0.81 | 0.806 | ||
Geniventure made me more curious about genetics | 49.72 | 1.12 | 1.18 | 0.826 | ||
I felt involved in this experience | 48.70 | 0.75 | 0.70 | 0.816 | ||
This experience was fun | 48.04 | 1.11 | 1.07 | 0.822 |
Appendix 2
Lists of Learning Objectives and Genetics Concepts Associated with Geniventure and Genetics Assessment.
Learning objectives
LG 1. There are predictable correlations between an organism’s genes and its traits.
LG 2. Genetic information is passed to an individual from both its parents via their gametes.
LG 3. Processes of inheritance involve randomized events that produce predictable patterns in offspring populations.
LG 4. Genes are instructions for constructing proteins.
LG 5. Proteins carry out a variety of functions in cells.
LG 6. Protein function is a result of protein structure.
Genetics concepts
-
1.
Sex determination (LG1.A3)
-
2.
Simple dominance (LG1.C2a)
-
3.
Recessive traits (LG1.C2b)
-
4.
X linked genes (LG1.C2c)
-
5.
Polyallelic (LG1.C2d)
-
6.
Incomplete dominance (LG1.C3)
-
7.
Genotype-to-phenotype mapping (LG1.P1)
-
8.
Phenotype-to-genotype mapping (LG1.P2)
-
9.
Epistasis (LG1.C4a)
-
10.
Gamete selection (LG2.P1)
-
11.
Parent genotypes (LG3.P1)
-
12.
Patterns in offspring (LG3.P3)
-
13.
Test cross (LG3.P4)
Appendix 3
The coding scheme for students’ game experiences (Adapted from Baker et al. 2010)
Code for category | Category | Subcategory | Definition | Example | Dimension |
|---|---|---|---|---|---|
1 | Displeasure/negative feeling/experience (k = 0.857) | Boredom | When participants expressed any weary feelings and no interest | “boring” [Student ID 234423] | High frustration due to interface and control problems |
Frustration | When participants expressed any dissatisfaction, annoyance, and other frustrating feelings/experiences | “The activities when you have to click the triangles to change the dragon color is very unclear and annoying” [Student ID 251491] | |||
Confusion | When participants expressed any noticeable lack of understanding | “On level 4.1 on the second gem, the amount of armor on the target dragon needs to be more apparent because I kept getting it wrong because I couldn't tell how much armor was on the target dragon.” [Student ID 252074] “Level 3.2 was not easy to understand. I really struggled with figuring out what I was supposed to do.” [Student ID 246228] | |||
Difficult | When participants expressed that the mission or challenge was too hard | “It was quite difficult to get a blue crystal at this point.” [Student ID 241121] “it took me a full hour to do the very last task. really hard to understand and no hints. would appreciate a help button for when you are stuck” [Student ID 235709] | |||
Display or other system problems | When participants expressed that they dissatisfied with display or interface, unclear directions and when they experienced any error or game issues | “The graphics were a little slow and some things were hard using the trackpad on the chromebooks, but we didn’t have another option” [Student ID 172936] | |||
2 | Neutral (k = 0.841) | Neutral | No apparent feeling or emotion, including “No,” “Nope,” and “Easy” | “There is nothing else I want to say about my experience with [game].” [Student ID 247450] | Most students said “No” |
Surprise | When participants expressed amazement or wonder from the unexpected | “the thing is how the dragon has horn” [Student ID 235814] | |||
3 | Pleasure/positive feeling/experience (k = 0.922) | Delight | When participants expressed any satisfaction, including with pleasure on visuals, difficulty, and other experiences. Also, this may include “Yes” and “OK” | “Really fun to learn and play.” [Student ID 234176] “…it was fun and challenging.” [Student ID 216324] “my experience was good because I learn about genes.” [Student ID 235803] | Students enjoyed playing the game and learned something from the game |
Engaged concentration | When participants expressed interest in the game resulting from the involvement in the game activity | “It was fun. Also it was a little challenging at first then I understood it.” [Student ID 234425] | |||
NR | When participants’ statements are not interpretable and do not fall under the above criteria | “I’m a reptiliologist” [Student ID 238430] “I don´t like the survey.” [Student ID 235590] | |||
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Rachmatullah, A., Reichsman, F., Lord, T. et al. Modeling Secondary Students’ Genetics Learning in a Game-Based Environment: Integrating the Expectancy-Value Theory of Achievement Motivation and Flow Theory. J Sci Educ Technol 30, 511–528 (2021). https://doi.org/10.1007/s10956-020-09896-8
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DOI: https://doi.org/10.1007/s10956-020-09896-8