Design for Learning and Assessment in Virtual Worlds
- 1. Brian C. Nelson Arizona State University August 2016 Design for Learning and Assessment in Virtual Worlds
- 2. Why Virtual Worlds? Early take: teaching/training is about information transmission and uptake by individuals More recent view: learning is primarily situated, social activity of collaborating to make sense of and apply content and concepts in specific contexts Many commercial virtual world-based games are based on social networks and collaborative problem solving…in and outside of the game environment Virtual world-based games are really good at collecting data about player activities
- 3. Why Virtual Worlds (2)? Virtual Worlds and other digital media fill every moment of a learner’s life…until they enter the classroom To students in a media-rich world, the classroom can feel like a museum Virtual worlds engage learners beyond a novelty effect Virtual worlds may support demonstrations of learning different/beyond that supported by traditional assessments
- 4. Educational Virtual Worlds: A study in Contrasts 1. Well -designed virtual worlds are good for learning Many educational virtual worlds are poorly designed 2. Virtual worlds can support innovative assessment of “21st century skills” Many educational virtual worlds use traditional measures and approaches to assess learning 3. Virtual worlds can support innovative thinking and multiple ways of knowing Policy and cultural issues result in virtual worlds that guide learners toward homogenous thinking and simple answers
- 5. My Goals Related to these Issues 1. Virtual Worlds are poorly designed 2. Virtual Worlds use ill- suited assessments 1. Theory-based design for learning in Virtual Worlds 2. Embed meaningful assessments Issue Goal
- 6. My Goals Related to these Issues 1. Virtual worlds are poorly designed 2. Virtual worlds use ill- suited assessments 1. Theory-based design for learning 2. Embed meaningful assessments Issue Goal
- 7. Challenge: Education researchers focus (understandably) on curricular and pedagogical issues Need: Study design of virtual worlds as implemented in educational settings Better Design for Learning in Virtual Worlds
- 8. Complexity in Virtual Worlds Visual and interaction complexity boosts immersion/embodiment but may hinder learning in school settings large body of literature on design principles from a cognitive processing perspective Summary: cut the cognitive fat Yet...successful virtual worlds are frequently highly complex, but players can cope with that complexity and learn Pragmatic approach: investigate the use of cognitive processing based design to balance complexity, short and long-term engagement, and efficiency
- 9. Chris Dede, Diane Ketelhut, Ed Dieterle, Jody Clarke- Midura, Cassie Bowman, and a whole bunch more people! River City
- 10. River City An Multi-player world to teach scientific inquiry and content skills to middle school students Students work in teams to discover why people in River City are getting sick Students gather data, form and test hypotheses More than 20,000 students have taken part
- 11. River City Origins National policy focus on real-world science practices Push to include science inquiry into the classroom But…realistic inquiry is difficult to teach and difficult to learn in the classroom Challenge: Create collaborative, situated inquiry experiences that engage more students in science, particularly those underrepresented in STEM fields
- 12. River City Interface: a mess
- 13. Cognitive Design Keep learner focus in the 3-d environment Reduce reading through “natural narration” Use visual and audio signalling techniques to focus attention Apply spatial contiguity principle Design for “essential complexity”
- 14. Individual investigations of context-based science problems in a virtual world Example 1: Simlandia
- 15. Voice vs. Text Chat collaboration Ben Erlandson (former ASU PhD student) led study people learn better when words are presented as audio narration rather than as on-screen text (Mayer, 2005) helps reduce a “split attention” effect Do students completing a science inquiry curriculum in a game using voice chat for collaborative communication... self-report lower levels of cognitive load show better performance on a science learning measure ...than students collaborating via text chat?
- 16. Results Cognitive load Voice-based Chat: lower levels of perceived cognitive load Learning almost identical performance for both groups Why? Everyone did well on the pre-test Assessment-performance mismatch
- 17. Diane Ketelhut, Catherine Schifter, Younsu Kim, Uma Natarajan, Kent Slack, Angela Shelton, (and many more folks) SAVE Science
- 18. SAVE Science situated assessment using virtual worlds for science content and inquiry Virtual world-based Game to assess learning of classroom curriculum in science Collect data evolving levels of understanding Enable students who don’t do well with standardized tests to better show understanding
- 19. SAVE Science Design Studies (so far) Visual Signaling Avatar Personalization Spatial Contiguity
- 20. Visual Signaling in Virtual Worlds Virtual worlds work well for learning, especially over long(er) periods of engagement Virtual worlds are initially confusing, especially for novice student gamers Low efficiency poses challenges to in-school implementations Low efficiency challenges assessment reliability and validity Visual signaling is used to guide players to relevant objects and locations
- 22. Sheep Trouble Module https://www.youtube.com/watch?v=uurufkuXu3s Assess students’ knowledge of beginning speciation/adaptation as well as aspects of scientific inquiry. New and old flocks of sheep Determine why recently imported sheep are getting sick and dying Apply understanding of speciation and adaptation
- 24. Measuring sheep
- 25. Visual Signaling: Cognition Visual Signaling: using visual cues (such as arrows) to direct learner attention to relevant information on the screen or page (or virtual world) Visual Signaling may lower extraneous cognitive load and/or increase germane load…letting learners focus on tasks rather than on interface (Merrienboer, 2008) Mixed record in past studies: often found to reduce self-reported cognitive load, but not always coupled with improved learning (Morozov, 2009; Chen & Fauzy, 2008)
- 26. Signaling Questions Can the use of visual signaling techniques reduce perceived extraneous cognitive load in a short game- based assessment? Can use of signaling increase the efficiency in a game- based assessment?
- 27. Signaling Study 193 7th graders Sheep Trouble: Assessment of Beginning Speciation Random assignment: signaling/no signaling Lower overall perceived cognitive load (p<.05) Increased interactions with sheep (p<.01) More measurements taken (p<.001) More records entered in notebook (p<.001)
- 28. Implications and Questions Use signaling! Why did signaling have a ‘sticky’ effect on interacting with objects? Would the value of signaling for efficiency be greater in a high search environment? (One with more visual objects on the screen at once?)
- 29. My Goals Related to these Issues 1. Virtual Worlds are poorly designed 2. Virtual Worlds use ill- suited assessments 1. Theory-based design for learning 2. Embed meaningful assessments Issue Goal
- 30. Emerging research on: Data-mining Statistical methods for making sense of learner actions Less research on: Design of tasks and “Work Products” of assessment supported by highly immersive virtual worlds Virtual world-based Assessment
- 31. Evidence Centered Design for Assessment in Virtual Worlds …and/or the Presentation Model aspect of ECD (Robert Mislevy)
- 32. Assessment Tasks and Work products Assessment in Game-based learning environments: many researchers and designers focusing mainly on ‘black box’ analysis of data output Virtual worlds are designed spaces Need full spectrum design for more meaningful data
- 33. Assessment tasks in Virtual Worlds Virtual world-based tasks support multiple evidence channels in isolation and in combination Provide complex and interwoven collection of work tools for assessment activities
- 34. Example: Global Evidence Channels Location/Movement (LM) Object Interaction (OI) Communication Activities (CA) Location tracking •X location visited •Time spent at X •Coordinates Movement tracking •Direction •Speed •Acceleration/deceleration •Teleporting Movement patterns •Order of movement •Movement as response •Movement strings over time Objects: •View •Select •Click •Manipulate •Pickup •Release Object Types: •Artifacts and inventory •Tools •NPCs •Humans •“intangibles” •Type •Speak •Response selection •Emote •In and out of character •Human and NPC •Goal-oriented vs. social
- 35. DATA-MINING BASKETBALL TROUBLE Shanshan Zhang and Slobodan Vucetic Department of Computer and Information Sciences Temple University
- 36. Basketball Module http://youtu.be/hrZVa2i-e5I Assess students’ knowledge of gas laws and related properties as well as aspects of scientific inquiry. Mid-winter basketball tournament Determine why balls at outdoor game don’t bounce well compared to indoor setting Apply understanding of gas laws (air pressure/temperature link)
- 38. Study 1. Create automated grading models that predict the number of embedded assessment questions a student will answer correctly based on her/his actions in the module 2. 187 students’ records analyzed 3. Analyze correlations between multiple-choice scores, within-game behavior, and free-text answers 4. Correlation of .5 (Pearson’s p) with human graders on predicting performance on in-game multiple choice and short-answer questions
- 39. Study 4 important and non-redundant features found: Distinct interactions with in-game objects Number of NPCs talked to Number of objects whose air pressure was measured Number of temperature measurements recorded in e- notebook Key task: discover that a decrease in the temperature of several gas systems (basketballs and balloons filled with air) is causing their pressure to decrease.
- 40. Example A graphical illustration of how our Classification Techniques are able to learn to distinguish between Advanced, Proficient, and Basic student evaluations (x indicates incorrect prediction) • Good predictors for grades are highlynon-linear • Spherical boundaries approximately indicate the student groups