Fully Learner-Controlled Environment

An interactive learning environment should provide a suitable environment for a learner to build the intended knowledge and skills on their own initiative. As stated before, after the modelling phase in LOZ, the system assumes that the learner has acquired sufficient mental strength to perform a task related to the basic mental state. This assumption is assessed using relevant formative tests. The learner may simply answer the question. Alternatively, they may ask for a worked example or revisit the previous lesson. If they opt to answer, they will be immediately notified of the result. If it is wrong, they may try again. In both cases, they may choose the feedback level they prefer. The system allows the learner to select his/her own feedback and next topic. Allowing the learners to choose their levels of feedback is not a new concept. While discussing the provision of increased learner control over the type and elaboration of feedback, Mason et al states, “This option

Level PASm - for Medium level answers

L1 Explain why the answer is correct & why some other answers (for which the learner _{indicated high confident for correctness) are wrong, Move to next level} L2 Explain why the selected answer is correct & why all the other answers are wrong _{Move to next level} L3 Explain why the selected answer is correct & why all the other answers are wrong, _{Provide topic related explanation, Move to next level} L4 Explain why the system’s answer is correct and other answers are wrong _{Provide topic related explanation. Give MCQ at the same Scaffolding Level} L5 Explain why the system’s answer is correct and other answers are wrong Provide topic related explanation. Compare with Z and UML, if applicable

Give MCQ at the same Scaffolding Level

clarification as desired” (Mason et al. 1999). Moreover, the scaffolding levels are

designed to suit potentially weak students. The learner can select a suitable scaffolding level at any time.

Enriched by the constructive learning theory, the environment described above is totally controlled by the learner. This type of CBL system was highly promoted by some researchers in the aftermath of so called failures of Learner models. Designing complex ITSs is considered wasteful and CBL systems are seen as merely cognitive tools (Salomon 1993; Jonassen 1995), which learners could manipulate to construct their own knowledge. Shute et al (1995) describe the history of this trend under the heading “1990s:

Great Debates” (p. 583). Implementing a fully learner-controlled system is not as easy as one may perceive. This type of CBL system may be beneficial for a highly cultured learner who can effectively control their learning process. It demands a higher level of meta-cognitive abilities such as reflection and self-regulation. Nevertheless, a question debated in CBL circles is whether the learners are ready to get benefits from this type of instructional strategy, or in other words, how much learner control is advantageous for learners? In an attempt to argue for the usefulness of a Learner model for the proposed system, some experiences of the leading researchers in this field will now be considered.

The benefits of using CBL systems for learning depend on various factors such as the domain type, learning-outcome and learner traits. For example, an exploratory learning environment is better if the learning-outcome is building a ‘functional mental model’, and for learners who are systematic and high-explorative. Shute et al conclude “… a midpoint between too much and too little learner control is probably the best bet as far as optimal ITS learning environment. Furthermore, it should not be fixed, but flexibly changing in response to the learner’s evolving needs” (Shute et al. 1995, p. 584). While commenting

on the expectation that educating students may be possible by just giving access to the information (especially the internet), Laurillard states, “The notion is often accompanied by the rhetoric of being student-oriented, or learner-oriented: it is as absurd to try and solve the problem of education by giving people access to information as it would be to solve the housing problem by giving people access to bricks. Part of the point of an education is to give people the skills and understanding to enable them to handle information” (Laurillard 1996, P. 6). In an attempt to argue for Learner Models while

compromising with constructive theory, Self states, “In general, there is a set of potential properties available, each of which is more or less desirable than others, and there needs to be some heuristic strategy for guiding the students towards events which, on balance, are more likely to lead the learner to encounter contexts which allow a continuing learning experience” (Self 1999, p. 360).

The desired outcome of using the proposed system in this research is a smoothly executable skill rather than building a functional mental model. To achieve this goal, as discussed before, an exploratory learning environment is not very suitable. Therefore, based on the foregoing arguments, it was decided that LOZ should accommodate some sort of Learner model which could assist the learners (at least while they learn through the scaffolding process). In the next chapter, the proposed design for the Learner model that provides limited adaptability based on locally intelligent artefacts will be discussed.

5.9 Refinement Unit

As argued before, a Refinement unit that automatically transforms Object-Z specifications to equivalent Java code may be incorporated in the system. This feature could extrinsically motivate learners. Additionally, the system may provide an online environment which leads learners through a series of refinements in order to get the final program code for their own specifications. This facility, if provided, would enable the learner to engage in a constructive learning process based on problem transformation with scaffolding. Both ZAL/ZED and MEMO-II use animation for teaching (Section 3.3). ZAL/ZED transforms Z to LISP, and gives a limited interactive environment for ‘try and see’ learning. MEMO-II transforms an algebraic formal language automatically to some high level languages, and is intended for learning programming. There is no full-fledged animation system for Object-Z (McComb et al. 2003).

Animation of formal specification is an important research discipline on its own. Moreover, providing an online environment for animating Object-Z notation requires much effort. Nevertheless, in phase-I of the design process, only multiple choice tests are used for assessments and free form questions are avoided. A totally new specification will not ever be created while learning in phase-I, and therefore, automatic translation is not required. The Refinement unit in phase-I may include a mapping from the existing

efficient searching algorithm.

5.10 Summary

An Instructional strategy for CBL systems based on problem transformation and scaffolding has been devised. The key aim of this strategy design is to alleviate the potential difficulties associated with learning complex subjects such as Object-Z notation. The system has two phases, and this chapter has discussed a design for the first phase. The second phase will be discussed in Chapter 9.

A survey was conducted to evaluate the effectiveness of using a popular formal method tool for learning a formal notation (Z). The survey shows that the tool is not suitable for learning Z (but, it is useful for its original tasks such as specification creation and type- checking). Thereafter, the key challenges specific for learning Object-Z, the exemplar domain in this research, were identified. Creating a formal Object-Z specification from scratch (based on the given informal textual description only) is a challenging task as it demands both OO abstraction and mathematical abilities. On the other hand, transforming a semi-formal UML model to an Object-Z model is comparatively easy, because a UML model is basically a form of abstraction of the original problem. Therefore, the transformation process requires mathematical ability only. Initially, learners are required to construct Object-Z models from the given UML models. Gradually, the UML support is withdrawn. A four-phase instruction model is used to realize the scaffolding process. The pre-condition phase is included to revise the necessary UML knowledge. A Refinement unit, that can also facilitate active learning through problem transformation, is proposed to keep extrinsic motivation high.

The architecture of the CBL system has been designed to facilitate the proposed instructional strategy. The domain model plays an important role in our design. Concepts to be learned are organized in a hierarchical structure. The notion of mental states is discussed. While learning a concept progressively, students go through a series of mental states relevant to that concept. Each mental state is considered as a scaffolding level. MC tests are used for formative assessments. All the other course materials such as MC test

items, distracters related to misconceptions, and the corresponding feedback material have been carefully designed to support the scaffolding process.

In the Mentor model, various options for pedagogical actions (combining feedback and curriculum sequencing), which reflect typical teachers’ actions in different situations, are organized in different tables. The options are ranked: the highest level option is designed for most needy students, whereas the lowest aims at gifted students. Based on the performance of formative assessment, the system could lead a student from one scaffolding level to another. Traditional MC tests are not capable of measuring performance accurately. There is no way to identify partial knowledge on a test item. Therefore, a Confidence-Based MC test schema has been designed, where more than two levels of performance measurements are possible (for example, correct, medium, and incorrect).

There are many ways to implement scaffolding. A fully learner-controlled system may let the learner decide everything: topics to be learned, scaffolding steps, and feedback level. This environment demands significant commitment from the learner and is not suitable for everyone. A Learner model, which provides appropriate assistance to individual learners, is desirable. The next chapter describes the Learner model of LOZ in detail.