KSP684 - Games and Simulations and their Relationships to Learning - Margaret Gredler

Introduction

• Experiential exercises that transport learners to another world
• They use their knowledge, skill, and strategies within assigned roles
• Dates back to 1600’s war games
• Since 1950’s games have become regularly used by military for training
• Minecraft - requires players to apply knowledge in an effort to succeed - MC = Fractions
• Games = competitive exercises where the objective is to win
• Simulations = open-ended evolving situations with many interacting variables.  Goal is for participants to take a particular role, address issues, threats, or problems as they arrive.  Decisions made by participants can change outcomes
• Simulation example:
• Tidepools (zoology) - students are researchers who predict responses of real tidepool animals to low oxygen
• Turbina - students diagnose problems in an oil-fired marine plant
• Characteristics:
• Adequate model of the complex real-world situation (fidelity or validity)
• Defined role for each participant - with responsibilities and constraints
• Data-rich environment that allows for students to use a range of strategies
• Feedback for participant actions in form of changes in problem or situation
• Problems aren’t well defined but rather ill-defined with several parameters

Conceptual Framework

• Two concepts in games and simulations analysis:
• Surface structure -
• Games: paraphernalia and observable mechanics of the exercise (clicking on icons, or drawing cards)
• Simulations: scenario or set of data to be addressed by participants
• Deep structure - psychological mechanisms operating in the exercise. Nature of interactions between the learner and the major tasks in the exercise and between the students in the exercise.
• Examples: student control, earning rewards, positive feedback, complexity of decision sequence (variables, relationships among decisions)
• Shared features between games & simulations:
• Transport students to another setting
• Require maximum student involvement through active responding
• Student control in action
• Differences in deep structure between games and simulations:
• Types of roles
• Nature of decisions
• Nature of feedback
• Academic games:
• Competitive exercises with winning as the objective
• Rules govern play and paraphernalia to execute play, such as tokens, cards, and computer keys
• From simple exercises to more complex contests
• Deep structure of games include:
• Competition among the players
• Reinforcement in form of advancement in the game for right answers
• Actions governed by rules that may be imaginative
• Rules may specify the point values of different clues
• Four Purposes of Academic Games:
• Practice and/or refine already-acquired knowledge and skills
• Identify gaps or weaknesses in knowledge or skills
• Serve as a summation or review
• Develop new relationships among concepts and principles
• ALSO - can be used as reward for hard work
• EXAMPLE - variation of Twenty Questions where students try to figure out an author, chemical compound, or historical event
• Design Criteria - well designed games are challenging and engaging, while requiring the application of particular knowledge or skills
• Winning should be based only on the demonstration of knowledge or skills
• Game should address important concepts and/or content
• Dynamics of the game should fit age and developmental level of players.  Questions can be weighted with different point values based on difficulty.
• No points lost for incorrect answers
• Should not be “zero-sum” exercises - instead of one winner there should allow for multiple - winning can be based on best strategy or collecting most points or reaching a certain criteria
• Problem with computer games: sound and graphics can be distracting.  Sometimes the sounds/graphics for incorrect answers can be more interesting so players will intentionally get questions wrong
• Advantages to games in classroom: can increase student awareness, provide opportunity to apply learning a new context.
• Current problem - lack of well-designed games for classroom setting
• Academic Simulations:
• Evolving case studies of a particular social or physical reality.  Goal, instead of winning is to take a role, address the issues, threats, or problems, and experience the effects of one’s decisions.
• Can take several directions, depending on the actions and reactions of the participants and natural complications that arise
• Differ from role playing, which are brief, single incidents, where participants improvise their roles.
• Simulations address multidimensional evolving problems, run 50 minutes to several days.  They use role descriptions including goals, constraints, background information, and responsibilities
• Deep Structure:
• Dynamic set of relationships among several variables that reflect authentic causal and relational processes. Relationships must be verifiable, for example: diagnostic simulation, in which students manage patient treatment
• Require participants to apply their cognitive and metacognitive capabilities.  They provide opportunities for students to solve ill-defined problems.  Givens, desired goal, or allowable operators are not easily identified or immediately clear
• Feedback on actions is in the form of changes in status of the problem or reactions of other participants. Example - medical students incorrect treatment of patient could kill them.
• Complex scenarios that can take any of several directions are necessary
• Key feature is bona fide role and consequences of actions for playing that role.
• Advantages:
• Time-consuming but provide advantages not found in exercises using discrete, static problems.
• Bridge game between classroom and real-world
• Reveal students misconceptions
• Provide information about student problem solving skills
• Two types:
• 21.2.2.3 - Experiential
• Developed to provide learning interactions in situations that are too costly or hazardous to provide in real-world setting.
• Social microcosms.
• Learners experiences feelings, questions, and concerns associated with their role.
• Complex evolving situation - participant is a functional component of the simulation
• Simulation must accurately represent the real-world situation
• Three Types of Experiential Simulations:
• Social-process
• Contingencies are imbedded to initiate action and various roles.
• Role cards for spaceship crash victims include 2 or 3 unrelated bits of information important for survival.
• Participants must listen carefully and have clear communication.
• Diagnostic
• Patterns of optimal and near-optimal decision making are expected for real-world situations
• Sequential nature of tasks link decisions to prior decisions
• Errors are compounded on top of errors as mistakes are made
• Data Management
• Teams manage business or financial institutions
• Causal model - relationships among quantitative variables
• Included are relationships among inputted data from participants and probability, liquidity, solvency, business volume, inventory, and others.
• Teams receive financial profile of a business or bank.  They make decisions and receive updated conditions.  They then make more decisions, and so on.
• Symbolic
• Dynamic representation of the functioning or behavior of some universe, system, or set of processes or phenomena by another system.
• Key defining characteristic is that students functions as researcher or investigator.  They test their conceptual model of relationships among the variables in the system. This is the major difference between experiential and symbolic.
• Role of learner is not a functional component of the system.
• 2nd major difference is the mechanisms for reinforcing appropriate student behavior -  experiential sims - student steps into a scenario where their actions change things - the learner is executing random strategies experiences the results of their behavior (i.e. killing a patient). Symbolic sims, there is a population of events or set of processes that are external to the learner.
• Students need to acquire relevant domain knowledge and essential research skills prior to sims - students should be proficient at creating mental models, testing variables, and revising their mental models.
• Two Types of Symbolic Simulations:
• Laboratory research simulations
• Student functions as researcher
• System simulations
• Student is troubleshooter who analyzes, diagnoses, and corrects operational faults
• Other Technology-based Exercises
• Problem-solving Exercises with Simulated Materials
• Particular topic with dynamic visuals
• Not a simulation because they are discrete (defined) problems instead of student interactions with data or system in an open-ended exercise
• Computer-based Manipulatives (CMBs)
• Combine:
• Specific computer-based tasks to be solved through experimentation by two-person teams
• Paper-and-pencil exercises
• Class discussions
• Another project - in science
• Physics problems with dynamic visuals (Think PheT Simulations)
• More demonstration than actual simulation
• Learner formulates hypotheses, designs experiment, interprets data, and implements the activities through systematic planning and monitoring.
• High cognitive demands are placed on learner if basic research and scientific discovery knowledge isn’t pre-established.
• Another Physics Problem Solving Approach:
• Using abstract symbols
• Student constructs experiments on the illustrations of variables
• Reflection on data and results
• Difference between these and simulations:
• Visuals illustrate discrete relationships, not a data universe or physical or biological system
• Abstract symbols are the components of the illustrated relationships.  Simulation Models are almost as good as the real thing
• Simulation includes actions of the participants CBMs do not. Student actions impact results
• Virtual Environments - Virtual reality
• Computer generated 3D environments that respond in real time to the action of the users.
• Require headphones, gloves, headsets, etc.
• Convey a sense of being present in the environment
• Create a particular setting and attempt to draw the player in.
• Are they simulations

Comparison of Experiential Simulations

Comparison of Symbolic Simulations

Research In Games & Simulations

• Designed for situations where a particular need has been identified. Example: healthcare
• Most developers report only anecdotal evidence of success or their personal impressions
• Educational Games:
• Key feature is ability to apply subject matter knowledge in a new content
• Underwater Sea Quest involves laws of motion - goal is for diver to find gold treasure while avoiding a shark
• Parent interest in games used in the classroom declines in later grades (it is accepted in elementary)
• Experiential Simulations
• Social-process simulations are often developed to provide experiences using language to communicate for various purposes.
• One suggested application is to provide environments for learning-disabled students to develop independent living and survival skills
• Diagnostic Simulations
• Participants take on professional roles that involve problem solving.  
• Majority found in higher education.
• Increase internalization of material
• Medical education uses these often.
• Important characteristic of the medical model is the identification of the effectiveness of the students’ problem-solving strategies.
• Four types of problem-solving approaches:
• Thorough and discriminating
• Constricted
• Shotgun - tend to score high on proficiency but low on efficiency
• Random - least optimal approach - tend to score low on proficiency and efficiency scales - students prone to using this should work with a mentor or peer
• Analysis of the characteristics of current management simulations:
• Most include some sort of international competition and address elements involved in making strategic decisions
• Deficiencies included:
• Did not force participants to deal with the conflicting demands of various constituencies
• Did not allow for the range of grand strategies currently taught in management courses
• Management simulations have become more robust and strategic in recent years - more industry, realism, and technology support. Now include global markets and global producing areas and finance options
• Early Data-management simulations:
• Focus on student behaviors
• Competitive disposition is not related to performance
• Winning teams were the most cohesive
• Students self-efficacy in using strategic management skills is not explained by the use of case studies
• Factors contributing to poor performance:
• Making calculations without first developing a coherent mental model or setting goals
• Reactive not proactive behaviors
• Failing to alter plans when they aren’t working
• Lack of meta-knowledge - “knowing what we know and what we do not know - this is essential to problem-solving
• Symbolic Simulations
• Microworlds - computer-based simulation of a work or decisionmaking environment
• Important for participants that the environment constitutes a system.
• In science - simulations are a means for discovery learning, replacement for expository instruction or hands-on laboratory exploration
• One study showed higher performance in groups that used simulations to predict outcomes - simulation served as feedback system only
• Discussion:
• Experiential and symbolic simulations continue to be developed  in different subject areas to meet different needs.
• Diagnostic and patient service simulations
• Business education
• Identify strengths and weaknesses in planning, executing, and monitoring approaches to complex problems.
• Effective troubleshooting is evaluated in many simulations - how efficiently do they find the answer - is it trial-and-error or truly knowledge based?
• Design and development must consider fidelity of simulation (how close to real-world is it). Required are:
• Qualitative and quantitative model of relationships among events in the simulations
• Materials and required actions that result in realistic approximations of complex reality
• Simulation example: The Digital Field Trip to the Rainforest
• One concern for simulation design is that no clear outcome in favor of simulations has been found BUT instructional theory supports these alternatives and indicates that they contribute to meaningful of content to students.

Design & Research Issues

• Early simulations include military and political planning
• Business and medical education
• Participants apply knowledge in their subject area to complex, evolving problems - culminating experiences, not teaching
• Interactive exercises (sometimes referred to as simulations) take on task of teaching basic content
• No difference between computer-based classes and control classes
• Key issue is two questions:
• Does the simulation meet the criteria for the type of exercise (symbolic or experiential)?
• What is the purpose of the simulation?
• Culminating application of knowledge?
• Students must acquire knowledge before simulation
• Interactive exercises expect students to infer the characteristics of a domain and implement discovery face MORE difficulties
• Absence of prior instruction leads to failure
• Could teach students to guess
• Prior to simulations students need to be taught the requisite skills
• Learned develop cognitive skills and self-regulatory capabilities
• Simulations should provide information that is supplementary to traditional instruction.
• Important Questions:
• What is the nature of the knowledge
• Why should this occur?