Introduction In the learning process, the learner's affective state plays an essential role that influences several mechanisms of rational thinking and learning (D'Mello, 2012; Picard, 1997; Duque Reis et al., 2015). Learners citing negative affective states (e.g., boredom) during learning activities are, in general, significantly more likely to show inadequate learning outcomes, because they often are not motivated and are not engaged in learning (Craig, Graesser, Sullins, & Gholson, 2004; Shernoff, Vandell, & Bolt, 2008). To motivate a student so that he/she performs learning activities with complete immersion, it is necessary that his/her affective state provide an optimal experience. This affective state is denominated flow, and it is a mental state of operation characterized by a feeling of energized focus, full involvement, and success in the task being performed (Csikszentmihalyi, 2013). One condition for attaining and maintaining the flow state is a good balance between the perceived challenges of a task and the participant's own perceived abilities to solve it (Csikszentmihalyi, 2014). A task that is too challenging or one that is not challenging enough may lead to frustration or boredom, hence demotivating learners. Thus, to define a learning scenario with specific learning objects that favors and maintains the learner's flow during educational activities, instructional designers need to have some understanding about the influence of these activities/objects in the affective state of a leaner. For example, one factor that affects the perceived challenge of a given activity is the difficulty levels of learning objects. In this case, if the difficulty level of a learning object (or sequence of learning objects) is not adequately connected with the learning goals and the current knowledge and skills of a student, the learning scenario will be perceived as too hard and frustrating, or as too easy and boring. In a collaborative learning scenario, the challenge of designing adequate activities and selecting learning objects is even harder. If the instructional designer selects problems and learning objects that are too difficult (or too easy) for students, it will hinder students' interactions, demotivate students, and lead students to not want to work in groups over time (Challco, Moreira, Mizoguchi, & Isotani, 2014; Isotani, Inaba, Ikeda, & Mizoguchi, 2009). For instance, consider a scenario where a student (the tutor) interacts with another student (the tutee) to solve a given problem (i.e., a selected learning object). In this situation, the tutor will learn by using his knowledge/skills to demonstrate how to solve a problem and the tutee will learn by following the tutor's guidance. If the problem is too hard or the sequence of activities is not created to help students to collaborate, the tutor will not have the sufficient skill level or knowledge to solve and guide the tutee in the resolution of the problem. As a result, the learning scenario will cause emotional distress in both tutor and tutee, and the desired learning outcomes will not be achieved. To support the design of better learning scenarios that are pedagogically sound and can keep learners in a flow state, it is essential during the instructional design process to take into account the level of difficulty of learning objects and to link learning objects with theories that describe leaners' growth. Unfortunately, this task requires specialized knowledge about instructional/learning theories, Flow Theory, and Affect Theory, and the skills to apply this knowledge in an integrated manner in order to select adequate learning objects and design effective learning scenarios that match students' abilities. To support the design of authoring tools that help instructional designers with the proper selection of levels of difficulty that keep the learners in flow, in this paper we propose a framework to integrate the learner's growth process and Flow Theory through a new theory-based model, named GMIF: Learner S Growth Model Improved by Flow Theory. …
['Geiser Chalco Challco', 'Fernando R. H. Andrade', 'Simone S. Borges', 'Ig Ibert Bittencourt', 'Seiji Isotani']
Toward a Unified Modeling of Learner's Growth Process and Flow Theory