Learning Theories and CBL Systems

The early systems such as PLATO (Bitzer et al. 1961) and TICCIT (Faust 1974) were

based on behaviourist theory. In particular, Merrill’s instructional theory (later called Component Display Theory (Merrill 1980)) provided the basis for the lesson design in TICCIT. A particular learning task was decomposed through analysis into specific measurable tasks and tests were designed to cover each subtask in order to guide the teaching process in drill-and-practice fashion. The theory avoids activities of mind. There is no explicit knowledge in the system. And the knowledge state or misconceptions of the students are not considered. Most of these systems were developed by educational theoreticians and many of them are still being used in real teaching tasks.

Later, with the introduction of AI to CBL systems, a number of ITSs were developed based on cognitive theory. For example, a variety of ITSs were developed based on ACT* theory (Anderson 1993) and used “model tracing” for student behaviour modelling (e.g. LISP Tutor (Anderson et al. 1985)). Expert systems are also used as a basis in some CBL

systems (e.g. GUIDON-(Clancey 1986)). Another approach based on cognitive theory is the “buggy model” (impasse or failure-driven) (e.g. PROUST (Johnson et al. 1985)). A

notable variation on this model is the “mal-rule approach” (e.g. PIXIE (Sleeman 1987)). These concepts will be elaborated further in Section 2.9.

As stated before, the term ILE (interactive learning environments) is generally used to denote all the kinds of learning systems based on active learning or constructivist learning theory. ILEs demand high-level context-sensitive guidance (Ramasundaram et al. 2005).

Students are encouraged to be active learners (Bergin et al. 2003). The system and user

are jointly expected to discover any knowledge in the environment (e.g. Exploring the Nardoo (Hedberg et al. 1995). In particular, CBL systems for programming that provide

visualisation, algorithm animation, and other graphical facilities may be categorised as ILEs. A special kind of ILE called the discovery world will be discussed next.

2.4.1 Constructionism and Discovery Worlds

Constructionism is an important instructional theory under constructivism. This theory supports active learning as it demands creational activity. It is also goal driven. Seymour Papert (1972), a proponent of Constructionism, maintains:

“Constructionism--the N word as opposed to the V word--shares constructivism's connotation of learning as "building knowledge structures" irrespective of the circumstances of the learning. It then adds the idea that this happens especially felicitously in a context where the learner is consciously engaged in constructing a public entity, whether it's a sand castle on the beach or a theory of the universe”

(Papert 1991, p. 1).

According to Constructionism theory, the following ten major factors affect a student’s learning process: Learning by Doing, Learning How to Learn, Diversity of Personal Status, Learning Climate, Learner’s Personality, Physical Intelligence, Self-Problem Solving, Intellectual Intelligence, Evaluative Feedback, and Learning from Tools and Equipment (Jitgarun 2004). Constructionism is the basis for a kind of ILE called

discovery worlds that provide an effective environment for active learning by engaging

students in creational activities (e.g. POLYGONS (McArthur et al. 1999)).

Problem Transformation is one of the constructionist instructional techniques that demands active learning and prerequisite knowledge. In this approach, learners are motivated to construct new models based on the given model. The transformation task

the modelling processes or the fundamental theory behind the models. Problem transformation will be further discussed in Chapter 4.

2.4.2 Bi-modus Learning Environment

Cognitivism supports the information processing model. Knowledge is stored in the system. Learners are individually guided in a pre-determined manner to acquire it. To give support tailored to the individual learner, the system keeps a model for each learner (Shute et al 1995). However, inevitably, this Learner model is not accurate. The system

usually has a significant control over the learning process. On the other hand, in the constructivist view, the learning system should provide a suitable environment for the learner to construct their own knowledge. The system cannot provide individual support. The learner will have control over the learning process, or in other words, the learner is fully responsible for their own learning. In a practical approach, Intelligent Learning Environments are designed by incorporating features based on both cognitive and constructive theories (see Section 2.2.4). McArther et al. refer them as mixed-initiative systems with locally intelligent models, and conclude, “Most effective learning environments include a mix of direct teaching, more passive support for learning together with substantial student choice (McArthur et al. 1999, p. 58)”. Examples of this approach

include Intelligent Discovery Worlds (e.g. SMITHTOWN (Shute et al. 1990b)),

Intelligent Programming Environments (e.g. INTELLITUTOR (Ueno 1994)), and Guided-Discovery Environments (e.g. (Elsom-Cook 1990)).

Those who favour the philosophy of situated cognition argue for a social and collaborative learning environment. In this view, the computer is considered merely as a tool (albeit a very important tool) to facilitate a learning environment (Jonassen 1995). The role of AI is considered to be marginal in the sense that it is required only to mediate any collaborative interactions (Paiva et al. 1995). Anchored Instruction is an instructional

approach closely related to this philosophy (CTGV 1993). The communication capacity of computer networks, internet and intranets, are yet to be fully utilised to create effective Computer Supported Collaborative Learning systems.

Human beings are innately motivated to learn new everyday knowledge. However, for academic learning, motivation plays a vital role. In the next section some key motivational theories are outlined