Chen-Chung Liu, iLearn Lab Graduate Institute of Network Learning Technology,

1. A constructionism framework for designing game-based simulations for supporting computational problem solving Chen-Chung Liu, iLearn Lab Graduate Institute of Network Learning Technology, National Central University

2. Collaboration Classroom The classroom contains six workspaces. Each group workspace was equipped with a LCD shared displays. The shared displays are used as boundary objects to sustain intimacy and share individual contributions.

3. 高中職多媒體教學中心規劃介紹 內壢高中 – 競合式互動未來教室

4. The recent works Creativity Narrative Problem Solving Collaboration Train B&P -- World-Wide Invitingness -- Transcend physical limitation -- Dealing with uncertainty -- Sharing and collaboration Animated Web sketch books -- Expressive flexibility -- Narrative nature -- Sharing and collaboration

6. 火車啟動 火車下坡經過此處煞車 begin PowerUp(55); end begin repeat(3){ while(true){ if(TrainPassMe()){ train0.Break(100); print("Break"); break; } } } end 火車經過此處三次煞車 begin repeat(3) { while(true) { if(TrainPassMe()) { break; } } } train0.Break(100); print("Finish"); end begin repeat(3){ while(true){ if(TrainPassMe()){ train0.ReleaseBreak(); train0.PowerUp(30); print("PowerUP"); break; } } } end 火車下坡經過此處 放開煞車，加速

7. Outline • Introduction • Related works • The constructionism framework • Method • Results • Conclusion • Implications

8. Introduction http://www.youtube.com/watch?v=J1B5iee31z0

9. Introduction – Computational Problem Solving Problem solving is one of the integral approaches to achieving effective and meaningful learning(Jonassen, 2004). Problem solving has been extensively applied to many subject domains suchas science (Linn, Clark, & Slotta, 2003), mathematics (Jonassen, 2003) and design (Jermann & Dillenbourg, 2008) as a means of promotinglearning in these domains. Considered to be the core competency of computer science education because computer science involves broad problemsolving skills, rather than purely technically centered activity (Kay et al., 2000).

10. Introduction – Computational Problem Solving However, novice programmers suffer from a wide range ofdifficulties and deficits. One of the major issues facing computer science educators is how to foster students’ abilities to solve problems with computer programs.

11. Introduction – Simulation Game • Simulation games on computers may be helpful in fostering students’ problem solving ability. • Such games simulate a model of a system or a process, and thus allow students to experience the scientific discovery process such as hypothesis generation, experiment designs and data interpretation (de Jong & van Joolingen, 1998)

12. Introduction -- – Flow Experience Games may facilitate a flow experience considered as a useful construct for improving problem solving. Many studies have confirmedthat experiencing a state of flow may foster students’ learning, as well as their exploratory behaviors (Hoffman & Novak,1996) In particular, the higher level of flow perceived by learners correlates positively with higher engagement in experimentation(Trevino& Webster, 1992) and flexible learning (Webster, Trevino, & Ryan, 1993).

13. Introduction – Game design • However, recent investigations intogame-based learning yield divergent results regarding the effect of the games on learning. • The question/answer games have limitations in fostering long-term motivation to learn and in-depth learning strategies. • Therefore, it is necessary understand how to integrate learning tasks into game-based learning systems to transform the learning activities into flow learning experiences.

14. Introduction – Our goal • Design guidelines for game-based learning from the perspective of constructionism • Construction as the goal, • Low threshold and high ceiling • Simulation of ideas • Scratch (Monroy-Hernández & Resnick, 2008), Alice (Dann, Cooper, & Pausch, 2006), Tangible Programming Bricks (McNerney, 2004), and the Greenfoot system (Kulling & Henriksen, 2005),

15. Introduction – The research question How novice programmers may learn in the game-based learning system developed with the constructionism framework?

16. Related works 2.1 Computer simulation for supporting problem solving

17. Computer simulation Provides an opportunity for students to learn by doing Increasingly applied to foster problem solving abilities in several scientific subject domains ex: a computer simulation application was designed to facilitate medical science students to analyze information, formulate working hypotheses and identify medical learning issues It can be helpful in improving the students’ understanding of complex concepts, inquiry strategies and self-learning abilities

18. But ! although simulations can be helpful in improving the understanding of complex concepts, students may not know how to interact with sophisticated simulations in order to solve a problem. • Students often interact with simulations simply on a superficial and playful level • Such superficial interaction is partly due to the fact that most students cannot solve problems without instructional support • Consistent with the finding of Holzingeret al. (2009):

19. 2. Related works 2.2 Problem solving in games

20. Computer games As negative learning experiences such as boredom and frustration are more likely to remain for a long period of time, computer games can provide a pathway to transforming the experiences into positive states so that students are more likely to engage in meaningful strategies to solve problems. • An effective approach to providing instructional supports in computer simulations to help students solve problems • Promote students to apply logic, memory, visualizations and problem solving, and, thus, can enhance learning • Have a significant impact on learning experiences:

21. Computer games games with immediate feedback, clear goals and challenges can constitute an approach to creating positive learning experiences. Shih et al. (2010) found that games featuring clear goals, rules, challenges and a sense of achievement can enhance collaboration among students. Lee and Chen (2009) also confirmed the positive effect of games on problem solving. The claim made by Kiili (2005):

22. Computational problem solving However, how we can design a game or a game-like system to enhance computational problem solving is still not sufficiently discussed. The goal of the study: To understand how constructionist’s principles may be applied to design game-based learning system? To investigate the influence of simulation games on problem solving in terms of both learning experience states and problem solving behaviors To obtain a clearer picture of the problem solving strategies adopted by students learning with simulation games.

23. The constructionism framework

24. The constructionism framework

25. Construction as the goal Using Train B&P to construct a rail model. And Program it! To learn the computational thinking skills, and think scientifically for generating a railway model.

26. Low threshold and high ceiling Several building blocks such as straight tracks, curved tracks, branch tracks and bridges to build a rail system. Resembles the manipulative building blocks of a physical toy, its threshold to construct is quite low Press the “g” key to start a train Or program code to build complex railways

27. Simulation of ideas Simulate the programs in the 3D environment Train B&P was developed with a physics engine Gravity, speed, acceleration, and friction, to simulate the behavior of railway systems in the real world

28. Method

29. Participants 117 first-year students in a university in northern Taiwan They were novice programmers who did not have rich experience in programming This study designed a simulation game for the students to learn anduse their programming knowledge to solve some contextualized problems which are related to the transportation control of a railway system.

30. The simulation game TrainB&P

31. Simulation of embodied experiences • TrainB&P was developed with a physics engine which could simulate the physics phenomena, such as gravity, speed, acceleration, and friction, to simulate the real behaviors of railway systems in the real world. • Tutorial and examples

32. Procedures develop programs to make a train in a railway model go three rounds and then stop where it set off. traditional(1.5months)-> learning experience survey ->game-based learning activity (two weeks)->learning experience survey

33. The evaluation of learning experiences Flow: perceived challenge = perceived skill Anxiety: Higher perceived challenge with lower perceived skill Boredom: lower perceived challenge with higher perceived skill Learners will be more likely to experience flow when the challenge of an activity matches their skill(Massimini, Csikszentmihalyi, & Delle Fave, 1988). The 3-channel flow model,(Csikszentmihalyi, 1975):flow state, anxiety state and boredom state

34. Survey for learning motivations The students responded to the two surveys before and after the game-based learning activity. The Motivated Strategies for Learning Questionnaire (MSLQ)(Pintrich et al., 1991) The MSLQ contains eight questions with a five-point Likert scale concerning the extrinsic and intrinsic motivations associated with learning.

35. 3.6. Activity logs Solution development: the students typed the codes or modified the codes in the program panel. Experiment: the students applied the simulation function of the game to verify the behavior of the programs they developed. Solution review: the students opened the program panel to review the program they developed without typing or modifying any of the program code. Solution reuse: the students copied code segments in the tutorial or in the programs, which they had already developed, to generate new solutions. Reading tutorial: The students retrieved existing examples, knowledge related to generic computational problem solving, or information about the building blocks in the tutorial.

36. 3.7. Data analysis • Comparative analysis: • Students’ motivation and perceived learning experience in traditional lectures and in the simulation game approach • Problem solving behavior analysis: • Sequential pattern analysis • How the students developed solutions through the five types of problem solving behaviors

37. Result

38. Results (learning experience) • Learning experience • The problem solving tasks given in the traditional • lectures perceived a high level of challenge (mean= 3.87, S.D. = .79) but a low level of skill (mean = 2.62, S.D. = .88). • Students expressed anxiety in traditional lecture approach • The students’ feedback in the simulation game setting reveal • that the level of skill (mean = 3.05, S.D. = .71) is closer to the level of challenge (mean = 3.48, S.D. ¼=.69). • The level of challenge approached the level of skill.

39. Results (learning experience) Flow states The simulation game may be helpful in promoting the positive experience of computational problem solving

40. Results (motivations) • Motivations 40 The game transformed the learning exepreince from an extrinsic motivation into a intrinsic motivation.

41. Results (problem solving behaviors) Students in flow states, compared to those in anxiety state, tended to apply solution reuse to solve problems.

42. Results (problem solving strategies) • learning -by-example: reading tutorial→ solution reuse → experiment • trial-and-error: solution development → experiment → solution review→ solution development • analytical reasoning: solution development → solution review 42

43. Results (problem solving strategies) • learning -by-example: reading tutorial→ solution reuse → experiment • trial-and-error: solution development → experiment → solution review→ solution development • analytical reasoning: solution development → solution review

44. Results (problem solving strategies) • learning -by-example: reading tutorial→ solution reuse → experiment • trial-and-error: solution development → experiment →solution review →solution development Students in boredom state did not frequently apply analytical reasoning approach to solve problem.

45. Results (problem solving strategies) • trial-and-error: solution development → experiment → solution review→solution development • analytical reasoning: solution development → solution review Students in anxiety state did not frequently apply learning by example strategy

46. Conclusion and implications This study proposes a constructionism framework for designing computer game to assist students in developing their computational problem solving abilities. It is found that the students’ intrinsic motivation was enhanced when they learned with such constructivist approaches. The students were more likely experience a flow state when they learn with the game.

47. Conclusion and implications • Students may apply different problem solving strategies in a simulation game according to their learning experience states. • For the students who felt a flow experience, • Learning by example, analytical reasoning and trial-and-error strategies • For the students who feel anxious about simulation games, • it is necessary to provide instructional support to alleviate their anxiety. • For instance, to help them learn by examples.

48. Conclusion and implications • For students who feel bored • The teacher may increase the complexity of the problem according to the ability of each student so that the student may need to analyze the solution critically in order to solve the problem.

49. Thanksfor your listening!