Zualkernan, I. A. (2006). A framework and a methodology for developing authentic constructivist e-Learning environments. Educational Technology & Society, 9 (2), 198-212.

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A framework and a methodology for developing authentic constructivist e-Learning environments

Imran A. Zualkernan

School of Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, UAE Tel: +971-06-515-2953

izualkernan@ausharjah.edu ABSTRACT

Semantically rich domains require operative knowledge to solve complex problems in real-world settings. These domains provide an ideal environment for developing authentic constructivist e-learning environments. In this paper we present a framework and a methodology for developing authentic learning environments for such domains. The framework is based on an ecological view and characterizes dimensions of a typical constructivist environment in terms of pedagogical design, architecture, the environmental context and what is actually learned. A case-study illustrating the use of the framework to develop a just-in-time game-based learning environment is also presented.

Keywords

Computer assisted instruction, Expert problem solving, Operative knowledge, Development methodology Introduction This paper focuses on learning that occurs in professional problem solving domains that require a very high level of skill. In some sense, these domains are characterized by knowledge that is “operative” as the professionals in these domains are required to do work in real settings. This paper presents a framework that serves as the foundation for conceptualizing the development of authentic constructivist environments in such domains. The constructivist view of learning has its foundations in Piaget (1975) who believed that learning is not transmitted passively, but attained through well-defined stages by active participation of a learner. Vygotsky (1980) presented similar ideas but focused on the importance of socio-cultural activity in learning in addition to introducing flexible stages of development. More recently, the importance of context and “authenticity” in learning has been emphasized by Brown, Collins and Duguid (1989). According to them, “authentic activity is the ordinary practices of cultures.” (p. 36). Lave and Wenger (1991) further extend this view in their influential work on situated learning to point out that, “…Learning occurs through centripetal participation in the learning curriculum of the ambient community” (p. 100). Where the learning curriculum consists of “…situated opportunities (thus, including exemplars of various sorts often thought of as “goals”)” (p. 97). For the purpose of this paper, constructivist authentic learning environments are defined as those learning environments whose design is consistent with the principles of the more recent constructivist tradition on how people learn as exemplified by the works of Lave and Wenger (1991) and Brown et al. (1989). As Herrington and Oliver (2000) point out, such learning environments typically provide authentic contexts and activities, access to expert performances, and support multiple roles and perspectives. In addition, such environments also support collaborative construction of knowledge and promote reflection and articulation. Finally, such environments may include coaching and scaffolding by the teacher and provide for authentic assessment of learning within tasks. Constructivist learning environments are ideally suited to teaching professional problem solving. However, the complexity and ill-structure of such problem solving activity raises particular issues when it comes to actually building a constructivist e-learning environment. For example, what one means by an “authentic context” or an “authentic activity” in a particular problem solving context is often unclear. Prescriptive guidelines (Herrington & Oliver, 2000) like using “activities which have real-world relevance” or “ill-defined activities” or “a single complex” task to define an “authentic activity” attack such difficult questions at a syntactic level and as such provide only a good starting point. Constructing authentic learning environments in professional problem solving domains requires an analysis at the semantic level based on a deeper understanding of what constitutes learning in these environments. This paper presents a framework and a methodology specifically developed for such complex domains.

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Framework The key construct of this framework is the concept of a “learning curriculum.” A learning curriculum consists of situated opportunities (Lave & Wenger, 1991). The principles emerging from this framework will be later used to prescribe the general parameters that govern the construction of authentic e-learning environments. In addition, a methodology based on these principles will also be presented. The primary components of the framework used to describe a learning curriculum are derived from (Johnson, Kochevar & Zualkernan, 1992a) as shown in Figure 1. Briefly, the “Physical Environment” is a description of the objectively observable characteristics (e.g., a disease or defect). The available information part of the physical environment may also consist of artifacts such as books, manuals, databases that exist as well as interaction with peers, experts and teachers. Specific characteristics of the environment require specific actions by the learner (e.g., diagnosis or repair). A task can only be performed by a learner because the information in the task is lawfully related to some physical occurrence (Turvey, Carello & Kim, 1981). The “Task Environment” is the subset of the physical environment that is relevant to a class of agents (e.g., surgeons). The adaptation is the primary construct in this framework and represents what is “learned” under the constraints of the task environment and the constraints from the learner. The constraints on the learner may contain cognitive constraints (e.g., short-term memory, processing capabilities (Anderson, 1990)) and learning styles (e.g., holistic, analytical, field independent vs. field dependent (Rumetshofer & Woss, 2003)) or based on theory of multiple intelligences (see Gardiner (1993), for example) on one side and goals and motivation on the other.

Figure 1. A framework for developing authentic constructivist learning environments

The first key construct in the framework is the “adaptation” itself. Adaptation is a construct that develops under the constraints of the task environment and the learner (this is similar to Simon’s notion of adaptation as the interface between the outer and inner environment (Simon, 1996)). Adaptation, however, does not exist inside the “head” or “mind” of the learner. It is a construct that represents what evolves as a set of routines (including asking for, and retrieving information, for example) or dynamics that allow the learner to be “fit” for the particular task environment. The second key construct of the framework is the concept of “fit” (this is not the same as the evolutionary biology’s notion of a fit, but it describes a psychological fit). Loosely, the fit describes how well a learner is adapted to the task environment (e.g., how good is the surgeon?). Fit can be classified into two dimensions; semantic and structural. The semantic dimension is a measure of how well the learner’s actions are acceptable in the particular environment (e.g., how well are surgeon’s operations received in the physical world? Or, how many patients actually die under her care?). Hence the semantic fit is primarily related to how well the goals and the intentions of the learner are realized in the actions he/she takes in the physical environment.

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The structural dimension of fit describes how closely the cognitive constraints and learning styles of the learner “match” to the information present in the environment (e.g., does the surgeon accept a particular type of surgery as suited to her/his skills?). Manifestation of failure of structural fit occurs when, for example, an individual refuses to accept the information provided in the environment as “valid” for their task environment. Authentic Learning Environments Authentic learning environments in the constructivist tradition are situations that allow a learner to create their own personal knowledge in a particular task environment. In simplified manifestation, an authentic learning environment is a surrogate to the actual problem-solving environment (e.g., surgery room as opposed to the surgery simulator). An authentic learning environment can more generally be described as a manifestation of a “learning curriculum” as described by Lave and Wenger (1991). In terms of the framework, the learning curriculum, then, is simply a set of situated opportunities that allow the adaptation to eventually attain a high degree of fit between the task environment and the learner. The design of a good authentic learning environment, therefore, consists of creation of an appropriate set of situated opportunities. Each situated opportunity is described by 4-tuple (I, A, C, G) where I: Information in the environment A: Successful actions in the environment C: Cognitive constraints and learning styles of the learner G: Goals and intentions of the learner A successful authentic environment has to create enough (and the right) situated opportunities to ensure that adaptation that arises for a specific learner has both a high structural as well as a semantic fit. A fundamental problem that arises with using authentic environments is their validity. How does, for example, one ensure that adaptation thus evolved within the authentic environment will in fact transfer to the real environment? The key problem that needs to be solved in developing an authentic constructivist environment, therefore, is to ensure that adaptation that emerges in response to the “fit” requirements (both semantic and structural) of the constructed authentic environment, also establishes a high degree of fit in the real environment. This is exactly what the framework and the resulting methodology helps achieve in a specific situation. Methodology This section presents the various dimensions of a typical authentic learning environment and shows how the framework presented earlier provides conceptual footing for a development methodology based on a) pedagogical design, b) architectures, c) the environmental context and d) what actually gets learned in such environments. These four dimensions culminate in a four step methodology where each step is tied to exploring one dimension. Step 1: Complete pedagogical design The primary objective of this step is to determine what constitutes a “situated opportunity” (i.e., I, A, C and G according to the framework) within a particular pedagogical design paradigm. A pedagogical design imposes broad constraints on what a situated opportunity can be. In doing so, it outlines the possible space of situated opportunities for the learning environment. In addition, based on the properties of the semantic and structural fit in the real environment, pedagogical design also determines constraints on the four components of a typical situated opportunity. Some commonly used pedagogical design paradigms for “authentic” learning environments in the constructivist tradition are given below (Ip & Naidu, 2002; Oliver, 2001)

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Problem-based Learning (PBL) - In PBL, a convincing scenario problem (scenario) is created where learners are supported by stories as told by various actors. The primary premise behind these environments is to allow the learners to fail in a “safe” context and to receive feedback as a third-person.

Distributed Problem-Based Learning (DPBS) brings the additional element of a group of individuals using the network as a medium to work on and solve a common problem.

Inquiry-based learning (IBL) is one variant of PBL that poses ill-structured tasks to the students. Role-Play Simulation and Game-based Learning (RSL) creates situations where learners take on the

role-profiles of various characters in contrived educational games. Case Studies-based Learning (CSBL) uses actual events to force students to “practice” on actual data in a

safe environment. Critical Incidence-based Learning (CIBL) occurs when learners engage in reflections on critical events

from their workplace. Project-based Learning (PRBL) engages students in designing and creating products that meet authentic

needs. No matter what the manifestation of the pedagogical design of an authentic environment, each has to pay particular attention to how and why the situated opportunities thus created are authentic. For example, in PBL, a problem consists of the information presented in the environment (I) and the feedback provided by the stories guides the learner on what is successful action (A) through failure. The problems have to be consistent with the goals of the learner (G) as well as the cognitive constraints (C). The DBPS simply adds additional sources of information (I) that learner can access. Critical Incidence-based Learning is particularly interesting in this context in that it is related to low-base rate tasks (Johnson, Grazioli, Jamal & Zualkernan, 1992b). That is, environments where the incidence of situated opportunities is very rare (e.g., earth quakes). In this case, the problem to be solved becomes mostly the generation of an appropriate number of situated opportunities, so that adaptation can attain a high degree of fit. Similarly, in CSBL, the emphasis is not so much on creating the right information (I). The appropriateness of actions (A) is also not a primary issue. The emphasis really has to be on how well the fit can occur between the learner’s cognitive constraints and learning styles as well as the goals. Step 2: Construct architecture for the authentic environment The primary objective of this step is to specify an architecture that provides appropriate support for the situated opportunities outlined in the previous step. The architecture of an authentic environment specifies the various components that must exist in a learning environment or a computer manifestation of it. A general characterization of the constructivist learning environments has been provided by Jonassen, Peck and Wilson (1998). The components needed for such environments are:

Problem/project space. The learners are presented with an interesting, relevant and engaging problem. This is simply the creation of a single situated opportunity.

Related Cases. When expecting learners to solve problems, they must be provided with a set of related experiences on which the learner’s can draw. These represent a set of situated opportunities similar to the one being presented.

Information Sources. Providing learners with information they need help with in a timely manner. This simply stresses the information component (I) of the situated opportunity.

Cognitive (knowledge) construction tools. Tools that support the learner’s abilities to solve the tasks at hand. These are a part of the physical environment (e.g., a paper and a pencil, a calculator, a utility program) if the fit determines the learner’s cognitive constraints (C) need to be augmented to achieve an appropriate fit.

Conversation (knowledge-negotiation) tools. Tools to support collaboration. These are a part of the physical environment, if accessing information (I) or successful action (A) requires external conversations.

Social/contextual support. Physical, organizational, political and cultural aspects of the environment. This can be primarily related to the motivation and goals (G) of the learner.

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Step 3: Consider context of learning The primary objective of this step is to ensure that the environmental constraints of the learning environment have been considered for the architecture proposed in the previous step. Authentic learning environments exist in a context. The environmental contexts of an intelligent tutoring system as described by Kinshuk, Opperman and Russel (2001) can also be applied to an authentic learning environment. The context is divided into seven categories.

Student (natural abilities, learning styles and motivation) Peers (interaction with fellow students) Social environment (social values, institutional values, evolution of common metaphors) Teacher (teaching styles, personality attributes) Discipline (homogeneity, operational/conceptual, physical/virtual, teaching traditions, levels) Characteristics of knowledge (operational, causal, contextual) Characteristics of medium (hardware, software and communication capabilities).

From the perspective of the framework, these categories roughly map as shown in Figure 2.

Figure 2. Relationships between framework and environmental context

It is interesting to note that the teacher or the teaching style maps mostly to providing the available information (what it provided to the student), successful action (guiding through assessment) and motivation. Most traditional instructional theories such as Gagne’s nine steps (Gagne, 1985), John Keller’s ARCS model (Keller & Suzuki, 1988) or Merrill’s ITT (Merrill, Li & Jones, 1991) can in fact be used to create these parts of situated opportunities as they rely largely on information (I) and successful action (A) and are generally concerned with how to enable a student to do something (as goals are prescribed by the teacher) Step 4: Clearly specify what is learned? The primary objective of this step is to understand and characterize the nature of adaptation that is expected to emerge from the authentic activities being carried out in the learning environment. One of the critical components of the framework is the nature of the adaptation that occurs as a result of interacting with the information, and carrying out actions in the physical environment. The adaptations that develop will be unique to a particular individual learner. While adaptation is an abstract entity, it can be described. In the context of semantically rich problem solving domains, descriptions of these adaptations can take the form of successful arguments generated by a learner. These arguments can be constructed from five basic types of backings (Johnson, Zualkernan & Tucky, 1993)

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Type 1 – This is based on analytic truths; the interesting property is that the all actions preserve global criteria of rationality such as consistency, soundness and completeness.

Type 2 – This is based on empirical judgments where the actions are constrained to consist of consensus among groups of individuals.

Type 3 – This is based on complementary representations of a problem and actions are constrained by agreement within complementary analytic representations and empirical judgments.

Type 4 – This is based on complementary representations of a problem and the actions are based upon resolving conflicts within these.

Type 5 – This is based “systems of knowing” that lead the process such that actions are constrained by self-reflection.

The adaptation for a highly skilled individual can be described using a complex combination of these arguments across diverse domains including medicine, chess, experimental design, VLSI manufacturing, and fraud-detection (Johnson et al., 1993). Bloom’s original description of the types of learning (Cognitive, Affective and Psychomotor) comes close to serving as an appropriate language for a description of such adaptations (Bloom, 1956). For example, the category of “synthesis” as described by Bloom (1956) consists of building a structure or pattern from diverse elements. The keywords that describe this activity are combines, plans, creates etc. The adaptation for a skilled individual engaged in a semantically rich domain such as statistical experimental design (Johnson et al., 1993), however, contains many types of “synthesis”.

Figure 3. A description of an “adaptation” tied to a particular goal

For example, the individual constructs an initial conceptualization of the client’s problem, subsequently she constructs a refined quasi-statistical representation (and re-representation) of the client’s problem, and then she constructs an appropriate design type and finally a specific design. The goal of carrying out each one of these “synthesis” steps actually are achieved by paying attention to very different types of information and by using various types of qualitatively different backings. For example, conceptualization of a client’s quasi-statistical representation of the problem is based on a Type 3 backing (see Figure 3) while the construction of an appropriate design type is Type 4. Derivation of a particular design uses Type 1 backing. Hence the argument structure described in (Johnson et al., 1993) presents a refined language to describe specific individual Adaptations for semantically rich domains as opposed to the classification based on keywords (e.g., combines, creates etc.) presented by Bloom (1956) to describe what is learnt. In summary, the methodology applied to a particular instance consists of exploring and refining the four dimensions in the context of the framework presented earlier. A case study is presented next to show how the methodology is applied in the context of game-based learning.

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Case Study – Just-In-Time Game-Based Learning This section shows how framework and the methodology based on four dimensions of pedagogical design, architecture, environmental context and what is learned was applied to the development of a just-in-time game-based learning environment. Game-Based Learning Game-based learning is a particular form of incidental learning that occurs while the learner is engaged in an activity that may not be directly tied to the task at hand. Schank and Cleary (1995) provide an example of such learning where in order to teach grade students about geography, the students were asked to play a computer game planning a driving trip to the city where their favorite team is playing. Incidental learning, in general (see Marsick and Watkins (2001), for example), has been shown to be a significant mechanism of learning. A detailed analysis of digital games for education is provided in Prensky (2004) that divides such games into the different categories ranging from facts, skills, judgments etc. Attempts at building game-based learning environments typically have fallen between two extremes. On one extreme are games like Virtual-U (Virtual-U, 2005) that simulates working of a university to teach administrators about strategy. In this instance, the operational rules as well as the constitutive rules (Salen & Zimmerman, 2003) are embedded into the game (operational rules of a game are the surface rules while the constitutive rules represent the deep mathematical structure that underlies the game). The end-user, however, has a limited choice in which set of constitutive rules to use by selecting the scenario to be of a particular type of a university (public vs. a private university, for example). On the other extreme are game-engines (see Crookston (2005), for example) that use the cover-story of an existing game format (corresponding to the operational rules of the game) such as the T.V. game show “who wants to be a millionaire?” or more traditional games like a cross-word puzzle where one simply inserts the syntactic elements of the particular domain in the game. For example, a game teaching biology is generated by only using biological terms in a cross-word puzzle. At the level of constitutive rules, there is no deep logical relationship between the game and the domain of learning. For example, in a cross-word puzzle, the two words “bacteria” and “hormone” may appear together just because they have the letter “e” in common and not because of their biological relationship. The case study presented here applied game-based learning in the highly contextual problem solving domain of software engineering. In this domain, complex software designs are constructed and re-constructed at a very rapid pace. One critical problem of learning in these environments is to be kept updated with the continually changing designs. These environments are particularly suited to automated just-in-time learning because of the rapid rate of change and the immense time and cost involved in constructing any learning aids by-hand. In addition, since these are semantically rich problem solving domains, the generic game engines that mostly capture the operative (or surface) rules of a game (e.g., cross-word puzzle) may not be sufficient to capture the richness of contextual learning that needs to take place. The focus of study presented here was on software engineering design processes that typically produce by-products like the UML diagrams (Jacobson, Booch & Rumbaugh,1999; OMG, 2005). An activity diagram is a UML diagram that represents a flow of activities (see Figure 8, for a partial example). This section presents an application of the methodology to the problem of constructing a constructivist e-learning environment for building and using activity diagrams in a typical software engineering process. Step 1: Complete pedagogical design The first and the most important step of the methodology is pedagogical design. From the perspective of the framework, each instance of a game-play is a “situated opportunity.” The key to designing a game-based e-learning environment is to ensure that the adaptation that emerges as a result of playing the game will also lead to successful actions in the software engineering domain. In order to do this, the goals and motivations of the engineers have to be considered (G). Similarly, successful action in this instance consists of actions that will lead to winning a game (A). In addition, the available information or cues provided in the game (I) should be

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consistent with the learning styles and cognitive constraints (C) of a typical engineer. The pedagogical design is carried out by considering the properties of the semantic and structural fit to determine what represents an appropriate “situated opportunity” (i.e., I, A, C, G) in the game environment. Semantic fit ties successful action to goals and motivation. Semantic fit, in part, determines the nature of adaptation that will occur. In the case of software engineers using activity diagrams in their normal work environment, an important determinant of successful action is their ability to predict the relationships between the various activities. This is, in fact, one primary reason for an activity diagram to exist; knowing which activity happens before which and under what conditions. For the game to lead to an appropriate adaptation (one that would also lead to successful action in the actual environment), this meant that the successful action needed to be tied to the ability of an engineer to predict the order and conditions of precedence of the various activities in an activity diagram. The game, therefore, had to be designed to ensure that more successful the engineers were at predicting relationships between activities, the greater the degree of their semantic fit. Structural fit ties cognitive constraints to available information in the environment. In addition to a specific activity diagram, information in the environment of a software engineer may consist of domain documents, other colleagues and even computer programs. Therefore, the information (I) provided in the game should act as a surrogate for the information in the real environment such that structural fit is high. Prior studies of software engineer’s design activities in their environment suggest that this information is local in nature (Zualkernan, Tsai, Jemie, Wen & Drake, 1992); due to cognitive constraints, engineers are only able to focus on “local” aspects of an activity diagram. In order to be consistent with this assumption on the structural fit, therefore, it was decided that games should only “encode” local information about an activity diagram. The “locality” of information was implemented by providing limited threads of execution through the activity diagram itself. In order to accommodate multiple learning styles of engineers, it was also decided that multiple game formats should be provided. Step 2: Construct architecture for the authentic environment The second step of the methodology is to establish the architectural requirements for the environment. Once the basic parameters of the particular game were established (i.e., what constitutes a “situated opportunity”), the second dimension, architecture was used to further refine the design of this game-based learning environment. The problem/project space describes the process by which a single “situated opportunity” is created. In game-based learning, a situated opportunity typically consists of a single play of a game. In this context, because of the nature of the problem (rapidly changing software designs), the process by which a situated opportunity is created was considered at two distinct levels; the generation of a game for an arbitrary activity diagram and various repeated plays of this particular game (Zualkernan & Parmar, 2004). Related cases or ways in which a game was played successfully were also required to be a part of the architecture. Similarly, the architecture also required that information (consisting of the actual activity diagram) was also made available to the engineers. For analysis, cognitive tools such as paper or pencil or automated analysis tools for activity diagrams were also required. In addition, user forums and real-time chat facilities that allow software engineers to converse with fellow software engineers were also made a requirement. Finally, the ability to group engineers to provide a social context built around the playing of these games was also mandated. Step 3: Consider context of learning Contextual analysis is used to ensure that the resulting architecture from the last step does not miss any important part of its context of use. After establishing the basic architecture of this game-based learning environment, the contextual analysis was carried out to further fine-tune the design of the environment. It was recognized, for example, that available information (I) is tied to the student, his peers and the characteristics of knowledge. However, the teacher dimension was missed and needed to be supported as well; therefore, the dimension of a attaching a tutor was also added. Similarly, since goal and motivations are tied to peers and social environments, it was decided to add a peer-to-peer challenge component to the game where an engineer could challenge a fellow engineer with a particular game based on an activity diagram. Similarly, since the game also had to run on mobile phones with fairly small screens, in order to be consistent with the cognitive constraints, the number of activity diagrams appearing on the screen was reduced.

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Step 4: Clearly specify what is learned? The final and most crucial step of the methodology is to ensure that the adaptation that emerges as a result of interaction with the e-learning environment is consistent with what is required for successful action in the real environment. Since activity diagrams are semi-formal, the primary types of arguments constructed by software engineers in a real environment are Type 1. This means that all their actions need to preserve some global notions of consistency and soundness. In other words, if an engineer takes an action that indicates that activity A (say, Pour Coffee) “happens before” B (say, Drink Coffee), the game needs to apply a formal rule to enforce this. This implies that each game needs a model of the activity diagrams to make these determinations; it is necessary for the game to determine what constitutes successful action for the engineer. In a real design context an engineer may look at an activity diagram where activity A “comes before” B and activity B “comes before” activity C and then take the action that activity A “comes before” activity C. The game must also make the determination that in the presence of the information provided (A “comes before” B and B “comes before” C), A “comes before” C constitutes successful action. The game simply needs to preserve the same notion of “rationality” (Type 1, in this case) that connects information with a successful action. Practically this meant that each game needed to maintain an internal model of the activity diagram. Design Based on the analysis presented above, four different forms of common game types were constructed for this game-based e-learning environment. Each is briefly described below. Tetris The classical game of Tetris consists of a sequence of geometric figures falling down at various rates. The learning version of Tetris was created by mapping the geometric constraints to the precedence relationship between activities (Zualkernan & Parmar, 2004). So rather than matching the geometric constraints, the engineers are asked to match the precedence relationships between falling activities. The information provided in this environment, therefore, was a falling block with some precedence constraints. A player who was able to match these precedence constraints achieves a high-score or a high semantic fit.

Figure 4. A Tetris-Like game automatically generated for activity diagrams From the framework perspective, each situated opportunity in the Tetris-like game can be summarized as follows: I: Local precedence constraints between activities represented by falling blocks where each block has different types of precedence relations (see arrows in Figure 4) attached to it. A: Successful action consists of matching “local” precedence constraints (who comes before who?) in a very short amount of time by rotating the falling block. C: A small number of options of precedence constraints are provided and is suited to a visual learning style. G: Match as many precedence constraints as possible in order to prevent the blocks from building up.

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Snake The game of snake consists of a snake that gets progressively longer and faster (and hence difficult to maneuver) as it eats various bits of “food” that appear. The learning version for this game was created by mapping the “eating” activity to the precedence relation and the length of the snake. If the player picks the wrong precedence relationship, the snake gets longer, picking the right now, however, keeps the snake to be the same length. Therefore, a player who is familiar with the precedence relationship will, in fact, achieve a higher score. From the framework perspective, each situated opportunity in this snake-like game can be summarized as follows: I: Local precedence constraints between two activities represented by the head of the snake and the food (see Food and Head in Figure 5). A: Successful action consists of matching two precedence constraints (who comes before who?) in a very short amount of time by eating the food or waiting until the undesired food (violating the precedence constraint) disappears. C: A small number of options of precedence constraints are provided and is suited to a visual learning style. G: Match as many precedence constraints as possible in order to prevent the snake from getting larger.

Figure 5. A Snake-like game automatically generated from activity diagrams

Hexxagon The Hexxagon game consists of a field of hexagons, where the player is allowed to move his marbles to a region within green or yellow regions. Green regions multiply the original marbles; while a move to yellow regions allow one to “kill” an opponent’s marbles that happen to be in adjacent hexagons. All the hexagons are initially labeled as activities (see Figure 6). A move to a hexagon whose activity follows that activity in the current hexagon multiplies the number of marbles. This rewards the player for knowing the precedence relationship.

Figure 6. A Hexxagon-Like game generated from activity diagram

From the framework perspective, each situated opportunity in the Hexxagon game can be summarized as follows:

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I: Local precedence constraints between two activities represented by the adjacency of hexagons (see hexagons in Figure 6). A: Successful action consists of matching two precedence constraints (who comes before who?) and deciding which of the green or yellow move will result in better positioning. C: A small number of options of precedence constraints are provided and is suited to a visual learning style. G: Match as many precedence constraints as possible in order to kill the opponent’s marbles. Space Invaders The classical game of Space Invaders consists of a number of alien ships coming down from the sky. The objective of the game is to shoot them down. The constitutive rule of this game is simply that enough “bullets” will tear away and make an alien ship disappear. A learning version of this game is formulated by mapping the precedence relation to the sequence in which the activities (that now represent alien ships) are targeted. The activities have to be targeted in the right sequence of precedence to achieve a high score. From the framework perspective, each situated opportunity in the Space Invader-like game can be summarized as follows: I: Local precedence constraints between two activities represented by the alien ships (see Figure 7). A: Successful action consists of matching two precedence constraints (who comes before who?) in a very short amount of time by killing only those aliens that represent activities that precede each other. C: A small number of options of precedence constraints are provided and is suited to a visual learning style. G: Match as many precedence constraints as possible in order to prevent the aliens from descending

Figure 7. A Space Invader-Like game generated from activity diagram

Implementation All the four game types were implemented using Macromedia Flash (Macromedia, 2005; Neave 2005) and were hosted within a modified version of the open-source Claroline Learning Management System (Claroline, 2005). The learning management system provides the functionality of forums, groups, chats and messaging that can be tied to a particular game. In addition, a manager can assign games to various players. Also included are interaction between the engineer, his peers and a tutor. For example, the engineers can post on forums about tips on how to play a particular type of game better. In addition, a tutor can post exercises so that the engineers can learn the peculiarities of an activity diagram better before playing the game. Since the games thus generated are SCORM-complaint (SCORM, 2005), the score of each student is automatically tracked and delivered to the respective manager. Various resources related to the game including the actual activity diagram used to derive the game is also made available as information (I) that can be readily accessed by a learner while playing the game as shown in Figure 8. The player can pause the game, consult the activity diagram and get back to playing the game.

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Figure 8. Playing a SCORM-compliant game inside Claroline

Finally, to better support the peer-to-peer dimension of learning, the games were also implemented to run on mobile phones supporting J2ME (Sun, 2005) and Flash Lite 1.1 (Macromedia, 2005). This allows players to challenge other players on their own activity diagrams. For example, it is possible for a manager to send a game to an engineer from Claroline (integrated with the Ozeki SMS Server (Ozeki, 2005)) or from another mobile phone using the short messaging services (SMS). This score achieved by the challenged engineer is sent back to the challenger via an SMS message. Discussion Game-based learning was specifically chosen as a case study for presenting this framework because it represents an extreme case. Of all the pedagogical design paradigms of an authentic constructivist environment (i.e., problem-based learning, case-based learning etc.), game-based learning seems the most unobvious. For example, software engineers do not really play games of the type presented in this paper. Therefore, one could argue that since these games are not a part of the “cultural practice” of software engineers, learning environments built around such games are not “authentic.” This view is essentially a simulation stance towards “authentic learning” that assumes that an authentic learning environment ought to be a model or a surrogate of the actual environment and therefore necessarily contain the complexity of the environment. Constructing a complex environment, however, is not necessary for guaranteeing authenticity. For example, citing work habits of apprentice tailors from Lave (1988), Brown et al. (1989) who point out:

“This is not to say that authentic activity can only be pursued by experts. Apprentice tailors …, for instance, begin by ironing finished garments (which tacitly teaches them a lot about cutting and sewing). Ironing is simple, valuable, and absolutely authentic.” (p. 36)

The framework presented here shows that not only can specific slices of authentic activity (like ironing, for example) be isolated, but the “surface form” of the activity can be modified without sacrificing authenticity. This is essentially what the case study in game-based learning shows. The key point is that the learning environment needs to give rise to an appropriate adaptation under the mutual constraints of information, action, cognitive constraints and goals. And even though the surface activity of game-playing seems like simple repetition, when analyzed via the framework, this activity is authentic because the situated opportunities are semantically similar to what one expects in a real environment of software engineers. Semantically similar because the software engineer has the same goals (determining precedence relationships and higher motivation because game-playing is fun), similar cognitive constraints (locality, in this case) and varying learning styles (catered for by different game types) and finally, successful action in the game (higher score in the game) corresponds to getting an appropriate answer in the real environment. Another observation from the case study is that technology is required to implement much of the social interactions (e.g., peer to peer interaction, sharing etc.) is already readily available in pre-packaged forms and once the methodology is applied, the implementation is relatively straight forward.

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Finally, the methodology is neutral in that it only supplies broad guidelines. For example, Bloom’s taxonomy (Bloom, 1956) can be used to extend the low-level pedagogical design of the game-based learning environment presented in the case study. The environment currently only supports learning of precedence relationships mostly at “analysis” and “comprehension” level. However, the environment can easily be extended to higher levels like “application”, “analysis” and “synthesis” by simply constructing a different characterization of the situated opportunities. In summary, the case study shows that the framework and the methodology enable a designer to build slices of authentic activities at the semantic level. This methodology, therefore, does not dictate that a massive simulation or reproduction of the real environment be constructed for all authentic learning environments (although, it does not preclude these). This enables a designer to build authentic environments incrementally. In addition, the methodology frees up the designer to employ novel forms (like game-based learning) while retaining authenticity. Conclusion This paper presented a framework and a methodology for analyzing the development of authentic constructivist e-learning environments for semantically rich problem solving domains. The development of an e-learning constructivist environment has been conceptualized in terms of the creation of “situated opportunities.” These opportunities have further been specified in terms of information available, successful action, cognitive constraints/learning styles, and goals and motivation. A methodology based on pedagogical design, architecture, context of learning and an analysis of what was learned when developing such environments was also presented together with a successful application of this methodology to create a rich just-in-time game-based learning environment. Although the framework has currently only been applied in the context of game-based learning, this framework is being used in building other types of constructivist environments such as problem-based learning. One interesting outcome of this exercise has been that most technology required for such environments is readily available as seen by the various types of open-source software used in the game-based e-learning environment. What is required and needed are methodologies and frameworks to guide the development in novel instructional paradigms such as game-based learning. References Anderson, J. (1990). The adaptive structure of thought, Hillsdale, NJ, USA: Erlbaum. Bloom, B. S. (1956). Taxonomy of educational objectives (Handbook 1: The cognitive domain), New York, USA: Addison-Wesley. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18 (1), 32-42. Claroline (2005). Claroline : Open Source e-Learning, retrieved January 09, 2006 from http://www.claroline.net. Crookston (2005). Flash Learning Games, retrieved January 09, 2006, from http://itc.umcrookston.edu/index.asp?section=flashgames. Gagne, R. M. (1985). The conditions of learning and the theory of instruction, Chicago, USA: Harcourt Brace College Publishers. Gardiner, H. E. (1993). Multiple Intelligences: The theory in practice, New York, USA: Basic Books. Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48 (3), 23-48. Ip, A., & Naidu, S. (2002). Experience-based pedagogical designs for e-learning. Education Technology, XLI (5), 53-58.

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