Cocea, Mihaela

Gutierrez-Santos, M.

Magoulas, G.

English

Portsmouth University Research Portal (Pure)

A Case-Based Reasoning Approach to Provide
Adaptive Feedback in Microworlds
Sergio Gutierrez-Santos, Mihaela Cocea, and George Magoulas⋆
Birkbeck College, London Knowledge Lab, University of London
23-29 Emerald St, WC1N 3QS, London (UK)
{sergut,mihaela,gmagoulas}@dcs.bbk.ac.uk
Abstract. This paper presents a case-based reasoning (CBR) approach
to provide adaptive support in microworlds. Interaction in microworlds
is complex and unstructured, making the analysis of student behaviour
difficult and the provision of computer-based feedback challenging. Our
approach starts with the ellicitation of expected solutions to microworld
tasks (both valid and common mistakes) to generate a case base. This is
used to evaluate the actions of students and provide adapted feedback.
Keywords: case-based reasoning, exploratory learning environments,
microworlds, adaptive feedback
1 Introduction
Microworlds are a special kind of exploratory learning environments (ELE) where
students are allowed to create their own models/constructions, and explore their
properties and relationships. Providing appropriate support in such a situation
is crucial for adequate learning [1], but this is an challenging endeavour due to
the ill-definedness of the interaction. This paper proposes an approach to pro-
vide support for a microworld called eXpresser. The goal is to provide adaptive
feedback on-demand to alleviate the workload of teachers in classrooms.
Most cases of analysis and support for ELE are related to systems for learning
Physics [2–4]. There are also relevant works in the domain of Mathematics [5,
6]. Although these systems grant some freedom to students, none of them allow
to create new models/constructions. Our approach uses case-based reasoning to
provide feedback to students interacting with a mathematical microworld that
allows them to create new shapes and algebraic expressions from scratch.
2 The eXpresser microworld
The eXpresser is a microworld that allows students to create figural patterns
and link them with expressions. Creating and combining patterns and expres-
sions, students can create many different structures in the computer [7]. The
construction of these structures in the context of a classroom task scaffolds the
development of algebraic skills of the students (our target age is 11–12 years).
The microworld grants a lot of freedom to students, who may construct their
patterns in a multitude of ways, that range from valid ones (see Figure 1) to
⋆ The authors would like to acknowledge the rest of the members of the MiGen team
This work is funded by TLRP (e-Learning Phase-II, RES-139-25-0381).
off-task behaviour. The complexity and variety of possible approaches makes it
impossible to list all of them. Our approach is based on the identification of the
main possibilities with the help of pedagogy experts, using this initial case base
to judge the specific approaches of students and provide adapted feedback.
Fig. 1. Several examples of expected solutions for a ’footpath’ task. Each solution can
be constructed in many different ways (i.e. different actions lead to similar structures).
The internal structure of the patterns is highlighted here for clarity. In eXpresser all
constructions would look the same in the normal course of the task.
3 CBR for adapted feedback generation
The most important containers of information in a CBR system are the cases
themselves, each of them storing information about one problem and its solution.
The other knowledge containers are the similarity measures (used to compare
cases) and the adaptation mechanism (used to adapt solutions to a new problem).
In our approach, problems are possible construction strategies on the microworld
and solutions are feedback provided to learners on demand (see Figure 2). Con-
struction strategies are represented as series of shapes. Shapes are defined by
attributes such as position, colour, and relations to other shapes (e.g. there are
as many green tiles as five times the number of red tiles).
case


type
problem
(strategy)


shape
1


attributes
{
Pattern parameters
Flags
relations
{
Dependency rel. b/attributes
Value rel. b/attributes
Rel. b/simple cases
...
shape
n
solution
(feedback)
Fig. 2. Case structure. For a more detailed description of strategies and shapes, see [8].
When a learner working with the microworld asks for help, the learner’s con-
struction is processed into a new problem, i.e. strategy. This strategy is compared
with all the strategies in the case base to find the most similar one. Details about
the similarity metrics used can be found in [8].
If a perfect match is found, feedback is immediately generated as the solution
of the matching case. Otherwise, the case solution will need to be adapted to
generate the feedback. There are two types of cases in our approach, so there
are two different adaptation processes.
If the retrieved case is an “expected solution” (i.e. the student is probably
working in the right direction), the text of the feedback changes to give the
student an encouraging message and additional information is put in place to
highlight those aspects on which the student should reflect upon. This informa-
tion can be extracted from the similarity comparison between the case and the
students result. If the retrieved case can be a “common mistake” (i.e. the stu-
dent has a misconception frequently observed in practice according to pedagogy
experts) the feedback provided contains a common message developed by the
pedagogical team. This message makes the students reflect on past actions and
realise their misconception. In this case, there is usually no further adaptation of
the feedback because it is already specifically targeted towards one well-known
misconception. Lastly, it can happen that the student’s construction is not sim-
ilar to any of the problems in the case base (e.g. the student has made little
progress on the task, or she might have found a new perfectly valid approach,
which had not been considered by the design team). This situation is beyond
the scope of the system, so a message is handled to the human teacher.
The proposed approach makes it possible to provide adapted feedback in mi-
croworlds, where the unstructured nature of the interaction poses an important
challenge. We plan to extend the approach to other microworlds.
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Enhancing modelling of users strategies in exploratory learning through case-base maintenance. In:

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis.

Probabilistic student modelling to improve exploratory behaviour.

The ANDES physics tutoring system: Lessons learned.

Towards contingent and affective microworlds. In:

Using pattern construction and analysis in an exploratory learning environment for understanding mathematical generalisation: The potential for intelligent support. In: Int. Workshop on Intelligent Support for Exploratory Environments.

Why minimal guidance during instruction does not work:an analysis of the failure of constructivist, discovery, problem-based, experiential, & inquiry-based teaching.

A case-based reasoning approach to provide adaptive feedback in microworlds

A case-based reasoning approach to provide adaptive feedback in microworlds

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