The brief.

Our final assignment for ETEC532 was a group project over several weeks called the Collaborative Inquiry Project. It consisted of two parts: Part A was a collectively-produced annotated bibliography and literature review while Part B was an application of what we learned during the review process to a digital educational production. Our team investigated the theory of optimal flow, often a sought-after affect/effect of art practice but sometimes also a desired educational outcome framed as deep, unbroken focus. Below, you’ll find my contribution to part B.

Unfolding flow: Interfaces, interactions, & metaphors 

Part A: The Context

Flow theory has roots in cognitive psychology and strong connections with constructivism. Originally posited by Mihaly Csikszentmihalyi in the 1970s, flow theory helps us to understand the experience of optimal engagement and the individual and contextual factors that can generate optimal engagement or, flow (Schmidt, 2010). Flow can be described as a state of engagement in which one is totally engaged in the task at hand, and loses track of time and self with the merging of action and awareness (Schmidt, 2010). An important key for understanding flow state is the autoletic nature of the experience which is the result of an activity or situation the creates its own intrinsic motivation, rewards, or incentives, specifically without any outside goals or rewards (dos Santos et al., 2018). In other words, the result of the activity is motivating for its own sake.

Conditions of flow

Before heading into a specific perspective on how technology can generate or support flow, it might be worthwhile to look at the nine conditions that are usually present for optimal flow to be possible (Csikszentmihalyi, 1990). The conditions most commonly associated with flow are:

  • Control

    The activity is chosen for its own sake or is voluntary. The activity provides a sense of control.

  • Challenge

    The perceived challenge of the activity is high, but in balance with perceived skill.

  • Goals

    There are clear proximal objectives for the activity that are perceived as important.

  • Feedback

    Immediate feedback is provided that indicates when one has been successful or not.

  • Attention

    Attention must be focused, so the environment should be distraction-free and cognitive load should be manageable (Schmidt, 2010).

  • Loss of self

    The activity becomes so encompassing as to encourage us to forget ourselves and our current state, even when we're hungry or tired.

  • Loss of time

    Our sense of time disappears and as we forget time is passing, it is often perceived as going faster than normal, hence, "Time flies when you're having fun."

  • Autotelic

    Motivation is intrinsic to the result of the activity for its own sake. External motivation is not necessary or important.

  • Merging of action & awareness

    Our actions and awareness must merge together resulting in a complete absorption into the process of the activity.

Flow and technology

Our focus for this CIP is on how flow can be used both as a teaching and learning tool, and as a goal for teaching and learning experiences. So, the tools and technologies chosen to craft experiences that generate optimal flow are key to this process. In this portion of our CIP, we'll walk you through an analysis of the current use of technology in e-learning contexts that might generate optimal flow. Then, in the next section we'll provide a future implementation of unfolding narratives and interface design of e-learning in order to better understand how the conditions listed above can be deployed online for helping learners achieve and exploit flow experiences.

Dos Santos et al., (2018) in their investigation of the value of using flow theory to help support computer and education challenges conducted a sweeping review of the literature on flow theory and computers and education. The literature was revealed to have framed flow theory research in three ways: 

  1. Research on how flow state is identified during computer-based learning activities,

  2. Research on how learning activities are designed in order to generate flow states,

  3. Research on which flow models (of which there are several) have been used to analyze problems in computers and education. 

For our purposes in this section, we'll focus on the second body of research - the design of learning activities.

Findings for flow

Of the 57 papers analyzed by dos Santos et al., (2018), only 13 presented their techniques in the design of learning activities. Of these, 10 of the designs used game design elements in the learning activity in various ways. You might see why we focused on game-like experiences in a lot of this CIP given the way games lend themselves to generating optimal flow. Only one each focused on computer assisted learning systems and the balance of skills versus challenge in the design. The authors speculate that designing learning activities to specifically achieve flow state might not be available because of the complexity of these designs and that while studies have researchers who can measure flow, a different skill set is required to design a study that intentionally aims to create a situation specifically to generate flow. Given the nine original conditions for flow, it isn't hard to imagine why designing for flow in education might seem daunting. This is an important finding given that dos Santos et al., (2018) also find that empirical evidence suggesting the efficacy of flow in education is overwhelmingly positive. Positive consequences in these studies included increased exploratory behavior among students, an increase in the students' learning, increased satisfaction with the learning activity, and more in-depth reflection on the learning. These findings speak to the importance of designing learning for flow, and for finding ways to measure both flow state and its impacts.

Part B: The design

Given the current state of the literature and what we know about flow, this section will choose several elements from narrative and world-building to propose a future-facing way we might design learning experience so they generate flow states and create positive learning outcomes for learners. Inspired by Brenda Laurel and her seminal book Computers as Theatre (2014), we will unpack several design elements for co-creating and representing actions online (Laurel, 2014) and apply them to an online learning experience.

Why Laurel's philosophy in particular? Glad you asked. Reconceptualizing what computers can do was a pivotal point in the shift to web 2.0 interactivity. In education, we might not always bring our thinking into this dimension of interactivity. Laurel conceives of computers as enabling and representing actions involving both human and technological participants in her design philosophy thereby rethinking the actual human-computer interface. Her common ground perspective proposes a collaboration between the human and computer that unites the traits, goals, and behaviours of both, seamlessly. In other words, the tool, while a pivotal part of the process of learning, disappears into the action, uniting fully with the learner. For some, this merging of action and awareness is an important condition for flow state (Csikszentmihalyi, 1990; Norman, 1996). I proceed below with this principle in mind.

  • Create an opportunity for flow to emerge as a result of common-goal interface design.

  • Embed relevant, proposed criteria for achieving flow into a computer & education interaction.

  • Situate the interaction within the parameters of the arts & humanities as a design element.

  • Propose a way to generate optimal flow with an example of a common-ground interaction.

Below, you'll find a series of lesson patterns designed around activities that learners would engage with. Flow state occurs with action or work and so these designs are meant to be constructed using backward design principles where knowledge is given or explored with an activity in mind. The design patterns are based on game design structures like branching experiences, sidequests, or revisiting the same point of interest over again after completing new achievements. Below, we explore how to design an online lesson using that structure of revisiting a point of interest after new learning takes place.

Generating flow with H5P

Below, you'll find a prototype lesson based on the looping activity design pattern above using H5Ps branching narrative tool. The lesson engages learners in a stop-motion animation film project and uses the conditions, especially antecedents for generating optimal flow in keeping with Laurel's interaction approach to the human-computer relationship (2014). Click right to pilot the prototype!

Discussion

The above prototype lesson addresses the antecedents for generating optimal flow in the following ways:

Control: Present in opportunities to choose example videos, own group roles, and learning opportunities as well as full creative control over the creative project.

Challenge: Learning is introduced in stages that increase in difficulty, requiring learners to acquire and apply new skills in iterative loops, taking into consideration their preferences, abilities, and based on the idea of Vygotsky's Zone of Proximal Development.

Goals: Each 'loop' activity has a clear target goal leading to the filming of a stop-motion animation.

Feedback: The chosen tool allows for learner input in their learning process with feedback in catering to learners' preferences.

Attention: The tool is an all-purpose experience housing a wide variety of media and while used in a browser which might have its own distractions, is capable of becoming full-screen for a more immersive experience.

Human - Computer Interaction: With regard to Laurel (2014), the tool affords learners a chance to co-construct an experience. It does this by providing choice, and the embedding of an external tool to afford collaboration among group members and learning according to chosen roles. 

Finally, we'd said above that this is a proposed future way to integrate the antecedents for flow into education as well as proposing a way to create more sophisticated technologically driven learning experiences. H5P was used here as a prototype for creating a choice-based lesson but there are others that work similarly such as Twine, Moodle Lessons, Brightspace conditional releases, or even Articulate Storyline. The thing is that none of these really make the experience as smooth as an interactive game that has the same underlying game-like pattern and none of them ease the load of designing for differentiation and learner ability. Combine a lackluster end result with a lengthy development process at the front end and these tools become difficult to implement at scale. In the future, perhaps there might be a designated, purpose-built tool especially for educational versions of underlying game-like designs that enables a lighter development load and creates a smoother final product with optimal flow baked right in.

Section references

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.

dos Santos, W. O., Bittencourt, I. I., Dermeval, D., Isotani, S., Marques, L. B., & Silveira, I. F. (2018). Flow theory to promote learning in educational systems: Is it really relevant? Brazilian Journal of Computers in Education, 26(2). 29-59. https://doi.org/10.5753/RBIE.2018.26.02.29

Laurel, B. (2014). Computers as theatre. Addison-Wesley.

Norman, D. A. (1996). Optimal flow. Arts Education Policy Review, 97(4), 35-38. https://www.proquest.com/scholarly-journals/optimal-flow/docview/210998450/se-2 

Schmidt, J. A. (2010). Flow in education. In P. Peterson, E. Baker, & B. McGaw (Eds.), International Encyclopedia of Education (3rd ed., pp. 605-611). Elsevier. https://doi.org/10.1016/B978-0-08-044894-7.00608-4

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