Learning Object Systems as Constructivist Learning Environments: Related Assumptions, Theories and Applications
Brenda Bannan-Ritland, Nada Dabbagh & Kate Murphy
George Mason University
Introduction
Humans are viewed as goal directed agents who actively seek information. They come to formal education [and training] with a range of prior knowledge, skills, beliefs and concepts that significantly influence what they notice about the environment and how they organize and interpret it. This in turn, affects their abilities to remember, reason, solve problems and acquire new knowledge (Bransford, Brown & Cockling, 1999, p.l0)
Capitalizing on the goal-directed nature of human beings and their prior knowledge for the purposes of enhancing learning has been a continual challenge to educators and those who design and develop instructional technology applications. Learning object systems present yet another technology-based instructional delivery environment with exciting features and attributes that can empower learner-driven experiences and promote cognitive processing if pedagogical considerations are taken into account in their development and evolution. To this point, the majority of literature and applications related to learning object systems have focused primarily on technological attributes, metadata standards and system specifications issues such as levels of granularity and ensuring interoperability (Wiley, 1998; Singh, 2000). While these are important hurdles to overcome before wide-spread use of these systems can be obtained, it is also crucial at this point to consider the implications of learning object use and implementation in an instructional context prior to full-scale implementation of this technology.
Pioneers in the instructional technology community have begun to grapple with mapping sound instructional principles to the technical attributes of learning object systems for education and training purposes (Merrill, 1999; Interactive Media, 2000). However, many of these efforts have focused on integrating traditional perspectives on learning based in cognitive information processing and instructional systems design. Other efforts have incorporated these perspectives in the use of learning object systems for increased efficiency of the design and development workflow processes. Learning object systems are well suited for these objectives integrating with ease clearly delineated, traditional taxonomies of learning into these compartmentalized, searchable systems and capitalizing on efficient, reusable content in the often arduous instructional design and development tasks. While these efforts demonstrate appropriate consideration of pedagogical principles, to our knowledge, the incorporation of alternative perspectives on learning related to constructivist philosophy have not yet been considered for application to learning object systems.
By their very structure, learning object systems are flexible, dynamic and highly engaging technology-based environments. These systems have great potential to capitalize on the goal-oriented nature of human learning processes as well as allowing learners to associate instructional content with their prior knowledge and individual experiences as detailed by Bransford and his associates above (1999). To this point, the attributes of the system that would permit learner-driven, constructivist-oriented activities have not yet been fully explored and may reveal significant implications for the development of these systems. This chapter is an attempt to map constructivist principles to learning object systems by considering related assumptions of learning, corresponding
theoretical approaches, and instructional applications.
To begin, we review two examples of learning object systems based in similar theoretical approaches: the IDXeleratorTM grounded in Instructional Transaction Theory (ITT) and FountainTM based on instructional systems design and performance support constructs. The IDXeleratorTM, heavily focused on delivery of instruction, and FountainTM, primarily used for design of instruction, provide examples for considering the possibility of a learning objects system that can be used to induce cognitive processing through learner-driven participation and constructivist principles. We then, review different grounding assumptions about how learning occurs and several theoretical perspectives related to constructivist philosophy. Selecting two of these theories, we present in-depth descriptions of instructional applications based in constructivist principles that could inform the development of learning object systems and conclude by presenting projected features of a learning environment based in constructivism. Only through sound pedagogical grounding will learning object systems have the potential to be used as effective learning environments. We hope that this chapter extends the current thinking and development of learning object systems to consider these alternative perspectives and capitalize on them to establish rich environments for both teaching and learning.
Examples of Learning Objects Systems
Merrill’s Knowledge Objects and the IDXeleratorTM
To date, the majority of literature related to applying a specific theoretical framework to learning object architecture has been written by M. David Merrill and his associates at Utah State University. In applying theoretical constructs to learning object systems, Merrill departs somewhat from the analysis and component oriented instructional strategies of his previous Component Display Theory toward a more integrated, synthesis oriented approach that combines elements of instructional strategies into a more holistic representation of instructional transactions (Merrill, 1999; see
A natural fit to the technical attributes of learning objects, Instructional Transaction Theory (ITT) represents knowledge as objects and related elements or slots of these objects as the components of subject matter content (Merrill, 1999). Inherent in this definition is the perspective that acquiring subject matter content is equivalent to the acquisition of knowledge. ITT then, is a methodology for manipulating these objects and their elements that are represented as specific instructional strategies (see Table 2 for a concise review of the attributes of this system). In the explanation of his methodology, Merrill describes a traditional database model of computer-based instruction (CBI) where instructional strategies are embedded in the programming or authoring system code, dictated by the designer and cannot be easily re-used without major reconstruction. He contrasts this view of CBI with an instructional system that could potentially permit access to the component parts of content and the dynamic transformation or display of these parts in highly specified configurations.
In his thinking about an instructional system based on ITT, Merrill applies an algorithmic model of computing to instruction. In this view, knowledge is represented by data and instructional strategies are represented as algorithms. These instructional algorithms contain presentation strategies, practice strategies and learner guidance strategies needed for the learner to achieve the instructional goal. The algorithms dictate various formats representing specific instructional strategies within which a set of knowledge objects can be displayed for the learner.
Merrill details four types of knowledge objects including entities (objects in the world and can include devices, persons, places, symbols), properties (quantitative or qualitative attributes of entities), activities (actions the leaner can take to act on objects in the world) and processes (represents events that occur that change properties and are triggered by activities or other processes). The knowledge objects include certain attributes or slots such as name, portrayal (text, audio, video, graphic animation) and description (open compartment that could include function, purpose and may be defined by a user) and are housed in a knowledge base. Combined, the instructional algorithms prescribe the implementation of an instructional strategy and the interrelationships among the four types of knowledge objects impact the representation or attributes of the object. These system features create the potential for the reference, reuse and reconfiguration of knowledge objects in the knowledge base into learner-selected instructional strategy representations. Theoretically, the same knowledge objects configured in different ways could be used to construct different types of presentation, practice or learner guidance strategies.
In addition, relationships between objects can be calculated as well using a defined terminology of additional slots (location, part-of and has-parts). In this system, the designer needs to take care to accurately describe the object, and the learner needs only to choose the instructional strategy he or she prefers and the system can then automatically generate a presentation, exploration, simulation or practice strategy using the same content.
An elaborate instructional system, the power of this proposed technology and the corresponding methodology of ITT is in the generation of a variety of instructional strategy approaches with similar reusable content. Although constrained by system-controlled presentation of content with limited selection of instructional strategies by the learner, the system has marked value for those who may desire an automated approach to designing and delivering instruction. The precision of this architecture in identifying subject matter content is both its’ strength and weakness. While this type of system provides a solid framework based in cognitive learning theory, it is limited to Instructional Transaction Theory and the translation of instructional strategies or instructional algorithms related to that view.
An existing application that contains attributes of ITT and is used as a learning-oriented instructional development tool is the IDXeleratorTM detailed by Merrill and Thompson (in press). The IDXeleratorTM contains a library of configured instructional strategies including presentation, practice, learner guidance and knowledge structures directed toward a specific learning goal. Functioning as an enhanced authoring system, the IDXeleratorTM allows the designer to select a goal and instructional strategy, and the system then prompts the designer to input appropriate multimedia resources. Providing an instructional shell based on principles of learning, the runtime system is coupled with a popular authoring system called ToolbookTM. The IDXeleratorTM presents an instructor or designer-driven system that capitalizes on a structured shell of built-in instructional strategies in the hope of producing a higher level of sound design of instructional modules.
Both the knowledge object system proposed by Merrill and the existing IDXeleratorTM operate on similar principles based in ITT and structured algorithmic processes. Merrill advocates expanding this system structure or syntax based in cognitive theory to many different content domains including science and mathematics, taking a one-size or structure fits all approach in regard to theoretical grounding. He discourages adaptation of the system by the user by stating that it is better to have an established knowledge syntax (knowledge object components) rather than have user-defined knowledge components due to the specific nature of algorithmic computation. From his perspective on and the design of these systems, it makes sense that if users were permitted to define knowledge components or knowledge object attributes, this feature would limit both the capability of the system to access and use learning objects in an effective manner and the generalizability of the instructional strategies. However, there may be alternative ways to incorporate user-defined components and maintain interoperability other than strict construction of limited, specific instructional strategies that need to be explored in order to permit the learner greater participation in the instructional process. Currently, the knowledge object system and the IDXeleratorTM based in ITT do present potentially powerful instructional delivery and development systems with well-defined, specific attributes, but these systems do not permit additional learner involvement beyond selection of a pre-configured instructional strategy. The resulting instruction that is delivered to the learner is identical to computer-based instructional systems that do not involve learning objects. The knowledge object systems detailed by Merrill promote great flexibility and involvement by the instructor or designer in selecting the instructional goal strategy, and resources, but comparably little involvement or direction by the learner.
Interactive Media’s FountainTM Learning Database System
Corporations involved in developing technology-based training are tapping into the potential of learning object systems for reasons that include:
increased efficiency in regard to training development cycle times,
the potential for increased effectiveness and personalization of training, and
consistency in design and development tasks.
Another example of a learning objects system that has been developed for internal corporate use is FountainTM, a proprietary system developed and used by Interactive Media Corporation. The FountainTM system guides the design and development of technology-based training enabled by re-useable learning objects accessed from the system’s resource database. Although the current use of Interactive Media’s system is not as a learning management tool, the attributes of the system enable the development and delivery of reusable, content-independent strategies that lend themselves to the construction of flexible, modular learning paths that support a variety of learning and performance needs. In addition, this type of system could potentially provide learners the opportunity to generate and contribute resources, just as Interactive Media’s instructional designers develop and contribute resources to their database of instructional strategies, resources and visual interfaces in order to more efficiently produce technology-based technology training.
Interactive Media Corporation has capitalized on learning object architecture to develop a Learning DatabaseSM system called FountainTM which is a repository of discrete instructional units each comprised of a strategy, instructional content, media elements and a visual wrapper. Once designed, the objects can be selected and sequenced to address specific performance requirements of corporate employees. In a white paper describing the creation of these objects (Interactive Media Corporation, 2000), Interactive Media has constructed some unique definitions and representations of learning and knowledge objects and suggests that these elements differ in composition. The architecture of the system presents an open, eclectic approach for including various instructional design constructs and strategies. The structure of Interactive Media’s FountainTM system is heavily based in traditional instructional systems design providing some similarity to Merrill’s systems. However, the FountainTM system is also based in performance support principles which signals somewhat of a departure from the theoretical grounding of other established learning object systems. FountainTM provides the trainee or learner opportunities for presentation, application and evaluation of their performance on the job. Strong focus on the learner’s performance, in addition to learning objectives and the flexibility of this system to incorporate various instructional strategies presents another approach to the design of learning object systems.
The FountainTM system houses learning objects that represent short, concise performance-based lessons organized around job tasks and knowledge objects that contain complementary resources providing enabling knowledge in support of the specific job tasks. Learning objects are created by first establishing the identified performance or learning objective central to the target skill (i.e. handling objections to the sale of a product)[1]. Then, an appropriate instructional strategy is selected or developed to create templates of interaction related to that strategy (e.g., show an expert modeling a sales conversation, list common objections for various products, provide a simulation in which the learner identifies objections and matches them with appropriate responses, participate in an online collaborative role play, help the learner to write his or her own response to objections). Last, specific content is applied to these instructional strategy templates (e.g., different customer situations or different products). Similar to Merrill’s approach, the Fountain system promotes generalizability of content-independent instructional strategies, however this system permits the use of many additional types of instructional strategies other than those based in ITT.
A knowledge object contains similar elements and structure including the enabling knowledge or objective (e.g., supporting a sales task), an instructional strategy template (e.g., organization and format of product reference card), applied content (e.g., information about the specific product), and a visual wrapper. This onion-like layering of performance or enabling objective, instructional strategy, content and visual interface permits the creation of flexible components that can be reused or adapted in different instructional or training situations.
The knowledge and learning objects are tagged with IMS compliant metadata and cataloged in a Learning DatabaseSM architecture based on competency models (or key tasks the learner will need to do on-the-job) offering a high level design structure that permits matching objects to particular performance tasks. A customized curriculum can then be assembled with various learning and knowledge objects based on specific factors like job function or business unit. Users can progress through a personalized, prescribed path of learning objects directed toward a specific objective or can access any related knowledge objects during the instruction or at a later point while on the job in an on-line performance support system mode. In this way, the system permits both prescribed and flexible use of the objects for learning.
Currently, Interactive Media uses FountainTM internally to manage the workflow of the design and development of training products. The benefits of this system are increased efficiency of use and consistency of design and development resources that can be molded into different learning objects and reused for multiple training requirements. Interactive Media uses this system to create and house resources representing various design and functionality elements such as interface models, audio, video, graphics components, instructional strategy applications as well as programming components. Some elements are combined into object “shells” representing a particular performance objective and corresponding appropriate instructional strategies. This attribute of FountainTM has similarities to Merrill’s approach with IDXeleratorTM and ITT. Generalized from specific content, these shells can be adapted or reused for different instructional contexts (e.g. using an active listening shell for deployment in courses on interviewing, coaching or problem-solving)2 easily integrating an alternative interface or different media resources. This same type of generalized structure can be used at a more micro level as well in creating templates based on enabling objectives that represent various types of presentation and evaluation strategies or different ways to engage and involve the target audience.