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Diagnosing Disease Inside a Cell: Collaborative Learning Environments in VR

Listen and learn

Listen and learn : Research paper
Roundtable presentation

Tuesday, June 25, 2:45–3:45 pm
Location: 121AB, Table 5

Presentation 4 of 4
Other presentations:
Integrative STEM for PK-4 Preservice Teachers
Improving Confidence and Competence With STEM Content and Pedagogy
The Design and Evaluation of Protocols to Support Systemic Innovation

Dr. Meredith Thompson   Dan Roy  
The CLEVR project has created a VR cell exploration game. Two students team up, one in VR (Oculus Rift) and one on a tablet, to diagnose a genetic disease and select a therapy. This game is designed to fit into high school biology curricula introducing cell processes such as translation (RNA to proteins).

Audience: Curriculum/district specialists, Teachers, Technology coordinators/facilitators
Attendee devices: Devices not needed
Focus: Digital age teaching & learning
Topic: Augmented, mixed and virtual realities
Grade level: 9-12
Subject area: STEM/STEAM, Science
ISTE Standards: For Students:
Creative Communicator
  • Students communicate complex ideas clearly and effectively by creating or using a variety of digital objects such as visualizations, models or simulations.
Knowledge Constructor
  • Students build knowledge by actively exploring real-world issues and problems, developing ideas and theories and pursuing answers and solutions.
For Educators:
  • Manage the use of technology and student learning strategies in digital platforms, virtual environments, hands-on makerspaces or in the field.
Disclosure: The submitter of this session has been supported by a company whose product is being included in the session

Proposal summary


Education Research design
Overall questions
-How can we design environments that leverage the affordances of VR to enhance learning outcomes?
-What applications maximize the affordances of VR?

Virtual reality environments allow for two main types of learning: procedural and conceptual (Bossard et al., 2008); the learning goals, whether procedural or conceptual, inform how the VR environment is designed. When VR is used for learning and practicing procedures (e.g. surgery), learning is often measured through “near transfer” tasks; the learner demonstrates understanding by replicating the same procedure on a very similar model (such as a physical model). When VR is used to learn concepts (e.g. protein synthesis), it is often measured through “far transfer” tasks, when the learner applies the knowledge to a new context. Of course, a VR environment can contain aspects of both procedural and conceptual learning, and each would be measurable in different ways.

In this study we will use a three stage approach to gathering evidence of learning for our proposed VR environments. During the design phases of the project, we will use a design-based research (DBR) framework (Barab & Squire 2004). Specifically, we will explore the process of designing VR environments that can enhance learning outcomes. We will document the design process steps internally. Educators will give feedback about the VR environment at key points in the design process. These teachers will provide context in understanding how the VR could be embedded within a classroom environment.

Once the VR environment is developed, we will engage students in small scale pilot testing of the environment. During the pilot phase, we will collect qualitative data through interviews, observations, and learners’ activities within the VR. We will use the in depth information we gather about students’ experiences related to their activities develop a framework for relating action in the VR to learning outcomes (Shute et al, 2013). These data will also be used to design instruments that are specific to our learning goals to be used to study the VR environment going forward (Merchant et al, 2014).

As the environments are developed, we will do a small quantitative pilot study with a few teachers to explore how the VR environments are related to learning outcomes. Each class of students will participate in two VR units and one 2D or 3D unit. Learning and knowledge will be assessed through instruments we developed during the qualitative phase and near and far transfer tasks.


Our research process consists of design, pilot test, and small quantitative study. We have done a significant amount of design work already, and will continue to improve the technology and learner experience. We have also done significant usability tests, both with students in our target population (high school), as well as students slightly younger (middle school) and older (college). This fall, we will conduct a small pilot test and quantitative study with a few teachers and their students. We will use the results to improve the design.

Our data sources will come from surveys, both pre and post, that we ask participants to complete, from video and audio recordings we do of playtest sessions, from interviews that get at depth of thinking about cellular biology, and from drawings of cells before and after playing the game.

We will select students from a range of school profiles, including diverse SES.

We will analyze the surveys, interviews, video data, and drawings by coding for certain types of conceptions, collaborations, and attitudes.


Our hypotheses are that high school biology students:
-have misconceptions about the size and scale of parts of cells
-have shallow, brittle knowledge about cell processes
-have limited conceptions of the thinking scientists actually do
-have limited ability to communicate thinking about cells

and that playing our game will result in:
-a more accurate awareness of cell size and scale
-a deeper knowledge about cell processes
-an improved ability to reason and inquire about cells, via a diagnostic process
-an improved ability to communicate scientific thinking


CLEVR will advance knowledge in how best to use technology to develop collaborative, game-based learning experiences that develop students’ conceptual understanding of biology. CLEVR will contribute to the immediate need for developing students’ skills and educating students about STEM careers. CLEVR will increase interest in biology and STEM careers, including among student populations who may not see themselves as scientist or interested in science.


Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The journal of the learning sciences, 13(1), 1-14.

Bossard, C., Kermarrec, G., Buche, C., & Tisseau, J. (2008). Transfer of learning in virtual environments: a new challenge?. Virtual Reality, 12(3), 151-161.

Clarke-Midura, J., Rosenheck, L., & Groff, J. (2015, June). Designing games for learning and assessment: the radix endeavor. In Proceedings of the 14th International Conference on Interaction Design and Children (pp. 343-346). ACM.

Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., ... & Zhang, F. (2013). Multiplex genome engineering using CRISPR/Cas systems. Science, 339(6121), 819-823.

Creswell, J. W.; Clark, V. L. P., Designing and conducting mixed methods research. Wiley Online Library: 2007.

Fischer, K., & Goswami, U. (2010). The future of educational neuroscience. Mind, Brain, and Education, 4: 68–80. doi: 10.1111/j.1751-228X.2010.01086.x.

Goswami, U., & Szűcs, D. (2011). Educational neuroscience: Developmental mechanisms: towards a conceptual framework. NeuroImage, 57(3), 651–658.

Johnson, D. W.; Johnson, R. T., New developments in social interdependence theory. Genetic, social, and general psychology monographs 2005, 131 (4), 285-358.

Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students' learning outcomes in K-12 and higher education: A meta-analysis. Computers & Education, 70, 29-40.

Reality Shift (2016). A UX designer's guide to combat VR sickness.

Schneps, M. H., Ruel, J., Sonnert, G., Dussault, M., Griffin, M., & Sadler, P. M. (2014). Conceptualizing astronomical scale: Virtual simulations on handheld tablet computers reverse misconceptions. Computers & Education, 70, 269–280.

Schneps, M., & Sadler, P. (1987). A Private Universe. Annenberg Media (Vol. video). Annenberg Media.

Shute, V. J., Ventura, M., & Kim, Y. J. (2013). Assessment and learning of qualitative physics in newton's playground. The Journal of Educational Research, 106(6), 423-430.

Steinkuehler, C., & Duncan, S. (2008). Scientific habits of mind in virtual worlds. Journal of Science Education and Technology, 17(6), 530-543.

Stieff, M., Ryu, M., Dixon, B., & Hegarty, M. (2012). The Role of Spatial Ability and Strategy Preference for Spatial Problem Solving in Organic Chemistry. Journal of Chemical Education, 89(7), 854–859.

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Dan Roy, MIT

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