Procesos identificados para la producción

Hence, trade-off issues between usability and flexibility must be considered in the design process [Murray, 2003, Woolf, 2010].

Thus, as previously mentioned, designing these systems present a huge variability and not all feature combinations might be necessarily effective for learners. In this context, gamification and ITS theories and design practices should also be considered to constrain the design space based on such knowledge. In this context, assuming that a teacher intends to customize such a complex system with this huge variability for his/her own educational context taking advantage of the knowledge about gamification and ITS theories as well as gamification design practices, we could not expect from him/her to have advanced technical skills, for instance, on programming, artificial intelligence and/or software engineering.

As a result, to address these issues, we describe our third technical research question,

which is: “How could we design a computational solution considering gamification and

ITS theories as well as design practices to aid teachers deal with the high variability of customizing gamified ITS features in a simple and usable way and with no advanced technical skills?”.

1.4

Objectives

Considering the presented research questions, we present some theoretical concepts as well as important technologies that are used to target our technical problem. Then, we describe the objectives of this work.

The concept of Software Product Line (SPL) [Clements and Northrop, 2001] [Pohl et al., 2005], from software engineering research, has been drawing attention of academics and practitioners promoting to offer characteristics such as rapid product development, reduced time-to-market, quality improvement, and more affordable development costs. A software product line is a set of software systems that have a particular set of common features and that satisfy the needs of a particular market segment or mission [Clements and Northrop,

2001]. In comparison to other reuse strategies, for instance frameworks, services and

components, SPL may be more efficient since its reuse is systematically designed and there is a way to customize the production of software from a same family [Helferich et al., 2007]. In this context, considering the huge variability presented in gamified ITS and also

1.4 Objectives 10

the need to personalize components of all three perspectives (technological, pedagogical and motivational) of it, the use of an SPL-inspired approach appears to be appropriate and promising in order to aid the customization of gamified ITS.

Feature modeling [Kang et al., 1990] is one of the key activities involved in the design of SPLs. It is broadly used to support variability management of SPLs in order to represent common and variable functionalities of a software family as well as to be used to instantiate applications based on SPL. In general, a feature model is produced to represent the commonalities and variabilities of SPLs. In order to deal with the high variability of gamified ITS, such activity could be performed to identify common and variable features of these systems in a manageable way. However, a gamified ITS in a specific context may demand different requirements, pedagogical strategies and gamification elements. Thus, allowing a particular gamified ITS to be reconfigured at runtime to change, for instance, a pedagogical strategy, can improve the flexibility of a system to be adapted to fluctuations in teachers needs.

In this way, enabling the automatic analysis of feature models and hence providing the automatic management of the gamified ITS variability would allow automated

reasoning/changing at runtime. Thus, when comparing the mechanisms for automatic

analysis of features models (i.e., propositional logic based analysis and constraint programming based analysis) [Benavides et al., 2013], description logic (DL) based methods (i.e., ontology-based feature modeling) promise to provide improved automated inconsistency detection, reasoning efficiency, scalability and expressivity [Benavides et al., 2010, Wang et al., 2007]. In this way, to allow automatic analysis of gamified ITS feature model, an ontology-based feature modeling approach could also be used.

Ontologies have gained significant attention by the computer science community since they aim to solve one of the biggest problems that arises when using machines to reason on information generated by human agents – they try to reach the formal representation of a real domain by using computational systems [Hepp et al., 2007]. Ontology is defined as “explicit specification of a conceptualization” [Gruber, 1993]. It is “explicit” because of its classes and properties visibility. Conceptualization is understood to be an abstract and simplified version of the world to be represented. Moreover, ontologies can be logically reasoned and shared within a specific domain [Guarino, 1998]. Thus, ontologies are a standard form

1.4 Objectives 11

for representing the concepts within a domain, as well as the relationships between those concepts in a way that allows automated reasoning.

Ontology is considered as one of the most appropriate ways to facilitate the interoperability between heterogeneous systems involved in a domain of common interest. This is true especially because ontologies offer a shared understanding of a particular domain and a formalization that allows its data to be interpretable by machines [Hepp et al., 2007]. In this way, considering a variability model (i.e., feature model) of gamified ITS, the use of ontologies might be used to enable its management (i.e., reasoning) by different gamified tutors.

There is also a growing interest on the use of ontologies to address e-learning problems. Particularly, in the context of ITS, ontologies have been used to represent domain model concepts, to represent students’ modeling allowing automated reasoning, to interoperate heterogeneous ITSs, and so on [Al-Yahya et al., 2015]. As previously explained, gamified ITSs are knowledge-intensive systems that handle knowledge about the domain of the tutor, students’ behaviors, tutoring theories, and so on. Formally representing gamification and ITS theories by using ontologies could provide several benefits to the design of gamified

intelligent tutoring systems. It could allow the automated reasoning of all knowledge

manipulated by these systems, which could also favor machines to automatically handle it. It might also provide a standard representation for the infrastructure of gamified ITSs, which may enable the interoperability (e.g., to interoperate educational resources) between different architectures of these systems. Furthermore, it may also leverage the transparency of the theories used to design these systems as well as allowing representing design practices for applying gamification in ITS – i.e., the later benefits could be very useful to aid teachers customizing gamified ITS.

Due to the high cost for designing ITS, for many years, researchers are developing ITS authoring tools in order to speed up ITS development, to reduce production efforts, to decrease the level of ability needed to build ITS, to support good design principles, to increase the number and diversity of available tutors, to extend the number of participants in ITS development process and so on [Murray, 2003, Sottilare et al., 2015, Woolf, 2010]. Although researchers’ interests in the development of ITS authoring tools, the inclusion of a gamification model may require new authoring tools in order to effectively deliver gamified

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ITSs. In this way, the development of a gamified ITS authoring tool that automatically relies on gamification and ITS theories and on gamification design practices to constrain the design space of gamified ITS would leverage teachers’ participation in the design of these systems. To answer the research questions presented and considering the concepts and technologies previously explained, the main objective of this thesis is to design and implement an authoring solution in order to provide for teachers a way to actively customize gamified ITS features. This platform takes into account an ontology-based feature model to deal with the high variability of these systems at runtime as well as an integrated ontological model to consider theories and gamification design practices for designing gamified ITS. In following we present our specific objectives according to the research questions they are targeting:

RQ1: How could we identify and manage the variability of gamified ITS features?

(O1) Define a reference feature model for representing the variability of gamified

intelligent tutoring systems;

(O2) Conceptualize an ontology for representing feature models and represent the

reference feature model using it;

RQ2: How could we constrain the design space of gamified ITS making use of gamification

and ITS theories as well as design principles?

(O3) Identify evidence-supported combinations of game design elements that might

be more amenable to be effective for achieving particular behaviors in the e-learning domain;

(O4) Design and develop a gamification domain ontology considering theories,

frameworks, and design practices;

(O5) Propose and develop an integrated ontological model that connects the

gamification domain ontology with existing ITS ontologies;

RQ3: How could we design a computational solution considering gamification and ITS

theories as well as design practices to aid teachers deal with the high variability of customizing gamified ITS features in a simple and usable way and with no advanced technical skills?