Finding appropriate didactical approaches for a specific purpose in software engineering education is difficult. Our work focusses on a recommendation engine for teaching methods. This encompasses modeling teaching goals and suitable teaching methods. To that end, we translated Reich's pool of domain independent constructive teaching methods into a concept map which also includes educational goals or skills at which these methods aim. We started out from Means-End-Maps (ME-Maps), i.e. simple concept maps based on i* which aim at modeling goals and tasks to achieve these goals. Modeling Reich's pool of methods revealed several shortcomings of ME-Maps. This article presents experiences we made with ME-Maps, discusses necessary changes and extension, and outlines an editor to create such models. Our extension to MEMaps is expected to significantly improve readability and overview by providing a visual map to quite complex models. Further, such concept maps establish a basis for a goal-oriented search engine for teaching methods in software engineering education.
Nowadays, it is commonly accepted that successful learning requires advanced teaching methods which reach far beyond traditional instructive formats. In particular, a large variety of active learning methods has been developed over the years in pedagogy. Yet, instructors are experts in their particular domain, say in software engineering, but often lack a profound pedagogical background. Therefore, they need support in choosing appropriate didactical methods for a specific purpose. In order to offer a wider variety of teaching methods, thus enhancing interaction in software engineering lessons, useful didactical methods need to be modeled jointly with goals that they may help to achieve and experiences related to their application in a specific setting.
Yet, it is still an open issue which modeling notation is most appropriate for that
purpose, striking the balance between clarity and simplicity on the one hand and
sufficient expressive power on the other.
This is why we explore in more detail whether Means-End-Maps (ME-Maps) [
Our work is primarily directed towards building a goal-oriented search engine
which allows instructors to enter their intent and, in return, provides a set of teaching
methods ranked by their suitability to meet these goals in a given context. A crucial
component of our work consists in establishing a basis for recommendations on
didactical approaches in software engineering education [
In the following section, we summarize an adaptation of syntax, semantics, and pragmatics of ME-Maps to make them suit our needs. These adaptations are based on experiences made when modeling Reich’s pool of constructivist methods with classical ME-Maps. We also briefly highlight some features of a modeling tool for our variant of ME-Maps before a summary and outlook concludes the paper. 2 2.1
Concept maps in general are intended to capture domain knowledge by describing
concepts and their relationships [
Therefore it seems to be a good choice to employ a hybrid recommendation algorithm using concept maps for domain knowledge and association rules for context knowledge.
It is worth noting that we explicitly do not want to build a recommendation engine for the one and only “perfect” method in a specific setting, but providing the instructor with a targeted list of promising methods based on matching the primary and secondary goals as well as the context.
Transparent recommendations are more accepted than non-transparent ones [
We started out by trying to model Reich’s pool of constructivist methods with the
strict version of ME-Maps and the recommended CmapTools [
Tasks describe actions performed by participants involved in a specific method.
Methods are a special kind of task which represents teaching methods and plays a central role for our purposes. Methods are derived from the task element and are associated with additional attributes for classification and filtering. For highlighting teaching methods and better distinction from “regular” tasks, their label is printed in boldface type. 2.2.2
Quality nodes in the original ME-Map approach are intended to cover the concept of
soft goals from i*. They express desired quality attributes associated with tasks [
Soft Goals mainly represent competencies or intended outcomes [
Qualities in our definition represent quality attributes that a method or task requires in order to be performed meaningfully. To distinguish qualities from soft goals, their label is printed in bold-face type and in italics. 2.2.3
The generalizes link may be used to model more specialized variants of a task, method, or goal. Derived elements inherit all aspects from their parents and allow for the definition of additional aspects in a specialized context. Thus, replicated parts of the model may be avoided and redundancy be reduced. 2.2.4
The contains link express either that a task has multiple sub-tasks or that a goal has
multiple sub-goals. In contrast to the consists-of link described in [
The achievedBy link is semantically identical to the achieved-by link from the original ME-Map approach and is intended to be an equivalent of means-end links in i*. It indicates tasks respectively methods – which are derived from tasks – offering solutions for a parent task. Sibling tasks respectively methods are alternative means to the end represented by the parent task. 2.2.6
The requires link is used to describe required goals needed to achieve another goal or to carry out a task respectively a method. Since this concept is closely related to the concept of association links between tasks/methods and qualities, we replaced association links from the original ME-map approach by requires links. In contrast to association links, the latter link type is also directed to emphasize the roles. 2.2.7
Positive (+) and negative (-) contribution links are used to describe the impact of a task, method, or goal on the acquirement of a competency. These links have an outstanding importance for recommending suitable teaching methods. Since the influence of a given method to the achievement of a given competency is hard to express by a quantitative value, it seems more reasonable to use a qualitative value. This contribution can either be positive or negative, in contrast to i* with its contribution links break, hurt, some-, some+, help, and make. We also decided to not use a qualitative scale like -- or ++ since this might suggest higher precision, yet might cause vagueness if the criteria leading to a rating are not defined or disputable. A fine-grained qualitative scale would also aggravate the occurrence of semantic conflicts when distributed partial models are merged since it is likely that most instructors classify positive and negative aspects similar but weigh them in a different manner. There is also no unknown or neutral contribution intended, since it would have no effect on the generation of recommendations, but would only increase the complexity of the model at the expense of readability. 2.3
The CmapTools recommended for drawing ME-Maps in [
Since distributed modeling is important in our setting to create a joint method pool, all elements are tagged with a content-independent globally unique identifier (GUID). Partial models may be merged by creating an alias for multiple GUIDs. This approach allows us to retain the original models, which facilitates correcting wrong associations done by either an algorithm or an instructor, and it can be used to train the merging and recommendation algorithms by implicitly defining synonyms. 4
Modeling didactical methods and the outcomes that they intend to produce is an important prerequisite for supporting instructors in choosing those methods that best suit their needs. This paper explored whether Means-End-Maps are an appropriate notation for that purpose by modelling Reich’s pool of constructivist methods. As it turns out, ME-Maps cannot reasonably be used out-of-the-box since some notational elements do not really fit our needs, both syntactically and semantically. In particular, there is a semantic mismatch between ME-Map’s qualities and goals in an educational setting. Therefore, we propose a variation of ME-Maps that seems to be better adapted to the modeling requirements in the educational domain. As future work, this notation will be further explored in the context of an intelligent recommendation system for didactical methods.
Our research is supported by the German Ministry of Education and Research (Bundesministerium für Bildung und Forschung) as part of the project EVELIN under grant no. 01PL12022A. For additional information see http://www.evelinprojekt.de.