Opening Learner Models

In Chapter 3, various approaches and their related advantages and the limitations of externalising Learner models were discussed. Initially, the idea of opening Learner models to the learners was suggested by Self (1988), in order to share the burden in designing and maintaining adaptive Learner models. However, the current trend in research towards opening Learner models reflects contemporary learning theories. The constructivist and situated learning theories suggest that instructional design should include strategies for enhancing collaborative learning and meta-cognitive activities such as reflection. Furthermore, opening up a limited part of Learner models to peers and mentors has also been given much attention in recent research. This helps learners not

addition, by providing facilities for expressing their views and concerns to their peers and mentors, the act of opening Learner model strengthens the link between mentors and learners in automated learning systems.

If a learner feels that the system is not providing sufficient feedback (it is probable that the system ranked the learner higher than they should be), one reason for this might be that the learner initially over-estimated their ability in the pre-requisite lessons. For example, if a learner with a high SMS score (for example, 90%) failed in a medium difficulty level question (for example, 55%), the system may assume it is a slip-up, and will just inform the user why the selected answer was incorrect. Finally, it will give another opportunity to correct the error. Actually, in the above situation, the learner requires detailed feedback. To understand the reason for this problem, the system provides facilities to inspect the state of the Learner model and the decision making process. The explanation will be given to the learner, using every-day terms (for example, “Very Strong” instead of “90% Strong”). Later, the learner, being equipped with the information that the system has ranked them incorrectly, may wish to alter the Learner model. The usage of a fuzzy model makes it easy to visualise the system’s beliefs about a learner’s different strengths. The visual models may be annotated, using the related linguistic terms used in the fuzzy model. Explaining a fuzzy model in natural language is easy, since the model uses the same terminology.

For a particular MC test, assuming the difficulty level (D) is a constant and, after an answer is given, the performance (P) is also a constant (Figure 6.4). Therefore, altering the PAS level will only affect SMS. The PAS level decreases from five to one, as SMS increases from weak to strong. An experienced learner may easily verify that, at a given scaffolding stage, if their answer is incorrect, they used to receive detailed feedback when their knowledge level was weak. Therefore, they will know what to change and then what to anticipate and they can easily understand the possible impact of their intended changes on the subsequent behaviour of the system. For beginners, however, judging their required level of feedback PAS (effect-variable) would be easier than estimating their own strength of mental state SMS (cause-variable). Therefore, the interface (screen shots are shown in Chapter 8) allows the learners to use any one of both options to alter the SMS directly or, for an inexperienced learner, to set the desired PAS level so that the

SMS rate will be automatically adjusted. To give visual cues, the numerical values will be automatically annotated by relevant bar charts.

Being a fuzzy model, it is straightforward to visualise the impact on the system’s belief, due to any adjustments made by a learner. If learners feel later that they had rated themselves incorrectly, they may simply try fresh values. This trial-and-error feature not only reduces the burden of the Learner model, but it also allows the learner to have some control over the system’s decisions. These facilities, in turn, encourage the learners to reflect on their own learning progress.

Moreover, learners, who want to take the ultimate responsibility for their learning process, may wish to investigate the underlying fuzzy mechanism. As the Learner model is based on fuzzy logic, the rule application process can be seamlessly described in natural language. For a learner, it will be relatively easy to understand the fuzzy mechanism, compared to other numerical approaches, such as BN. The enthusiastic learners, if encouraged to use this facility, would be able to significantly enhance their self- confidence.

This system also keeps relevant information about the current and past learning activities of a learner. Learners can view their past performance, related to the concepts they have learned to date. They may check their stored SMS values, related to past learned concepts and, if they so wish, they may change it. However, to improve the system’s belief, the learner may need to convince the system. Bull (2004) terms this ‘negotiating’, which means that the system may offer some tests to the learner, in order to give them an opportunity to prove their ability. Negotiating is also possible if (genuine) learners want to degrade themselves. The learner may revisit a recently attempted MC test, and attempt it again, or process the feedback in different detail. They can also go through the past scaffolding processes and critically analyse their own decisions in MC tests.

Finally, if a group of students are involved, the learner may be able to compare her/his performance against the best, worst and average cases. This feature is also used by some other open systems, for developing reflection habits in students (Kay 2000). For the purpose of academic learning, going beyond situated cognition, Laurillard (2002) argues that multiple contexts are not sufficient and that learners need to be engaged not only with

By opening the Learner model to peers and mentors, a facility is provided to the learners to decontexualise their knowledge, not only in multiple contexts but also through social experience.

Other than opening the Learner model, the mentoring processes can also be revealed to the learners. Mentor related issues, such as the scaffolding stages, the number of scaffolding steps, marking schemes and feedback strategies can be opened. The learner may make use of this opportunity to harness the learning environment to suit their traits and abilities. The next section will discuss this issue in detail.

6.9.1 Opening Mentor Model

In LOZ, the Learner model has no explicit control on deciding the mentoring actions. The mentoring actions are hardwired in the tables that describe the different levels of PAS tasks. After a test, the system may decide whether to allow the learner to stay at the same level, move onto the next higher level, or to move to the next lower level. This decision is made based on different factors such as, performance of the student, difficulty level of the question and rank of the learner. In an extreme case, the system may allow the learner to move two scaffolding levels upwards. However, for this to happen, the system should rank the learner too high.

The scaffolding levels are selected to suit potential lower level learners. This arrangement may cause boredom for some learners (particularly, for gifted students). Different learners have different learning abilities and, therefore, it would be beneficial to allow each learner to decide their own scaffold steps that best meet their needs. Moreover, a gifted learner may skip the initial steps and also they do not need to start from the basic scaffolding state.

In LOZ, before or after learning a lesson, the learner can inspect the levels and types of scaffolding process, and s/he can also modify this process for a lesson (initially some learners may perceive the scaffolding levels simply as different difficulty levels). For example, a lesson ‘Visibility List’ in LOZ has three scaffolding stages (Figure 6.1). A gifted learner may change the starting stage to three and, therefore s/he can avoid the easy

early stages. Some lessons may have more than five stages. The incremental steps can also be altered. This facility will help learners to control their learning process, and in turn it will help them to develop their meta-cognitive abilities. If learners feel later that they had selected inappropriate start levels or stage steps, they may revisit the lesson and assign new values for themselves.

The learner may also be allowed to decide their type of feedback and its timing. In the context of confidence based MC tests, the marking scheme and intermediate calculations can also be revealed to the learners. In extreme cases, if they can make use of it, the learners may be given the entire design and coding material.