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Project Overview
As my thesis/capstone project for Interaction Design, I conducted a research project on the topic of self-directed learning.  In my first sprint, I used a broad range of research methods to discover and define my problem space, covering topics like education theories, flow state, the Window of Tolerance, EEG & brain wave technology and psychometric assessments. I interviewed post-secondary students who identified as self-directed learners and also collected photos of their work spaces. I synthesized insights  key research findings and developed low fidelity prototypes addressing learner pain points.

During my second sprint, I developed a psychometric assessment and conducted statistical analysis on the results; Developed and ran extensive usability testing sessions of digital learning resources while collecting brain-wave (EEG) and other bio-metric data.
During my bachelor studies at Sheridan, I held multiple roles as a learning assistant, tutor, and student technician from 2017 to 2020. Working with other post-secondary students for hundreds of hours revealed my own motivations for learning were rooted in the ability to pass that learning onto others. As I continued to become immersed in education I noticed a recurring pain point that nearly every student, including myself, encountered on a daily basis. A lack of motivation when we need to self-direct our own learning.
Initial Research Inquiry & HMW
While I had my own experiences as a student and tutor as a starting point for this project, I needed to understand the other perspectives in my problem space. I read ravenously for the first two weeks of my fall semester. I went back decades, reading journals and papers about the introduction of alternative education in the western world; The history of education during the later 20th century as the digital world increasingly entered the classroom; Surface (rote) learning and deep learning; And finally, Universal Design for Learning.

Based on this investigation I decided to further investigate specifically deep learning in self-directed learning. Throughout my literature review, I consistently come across deep learning as a key factor and universal process in all forms of learning. Based on this finding, I constructed my first How Might We statement to inform the next stage of research.

How might we facilitate deep learning processes in self-directed study?
Project Objectives
To begin building a holistic view of the problem space I developed some broad objectives to achieve in my early research. 

⬤ Understand the perspective of self-directed learners and how they interact with learning resources. 

⬤ Discover and understand the current challenges and real-world conditions under which learners self-direct and study. 

 Understand how self-directed learners react to and attempt to solve their current pain points and unmet needs.

By setting these objectives, I set early and lean goals to hit with my first steps that I could use to define the problem space. 

Given my on-going work as a tutor, learning assistant and mentor at Sheridan, not to mention being a full-time college student, I had some lived experience in the problem space. This previous experience enabled my initial objectives and research questions to be more refined than if I was unfamiliar with the space. Documenting these assumptions was something I took careful time to document before beginning primary research or secondary research in earnest.

Digital Learning On The Rise
Digital Learning gives people unprecedented access to information and learning resources. Services like LinkedIn Learning, Udemy, Digital Tutors, and other platforms are rising to meet the increased demand for instructional content and dynamic learning outcomes.

Many Reasons For Self-Directed Learning
Self Directed Learners have many reasons for advancing their own education. Some of those reasons are: A passion for the topic, a need to supplement their learning, lack of faith in instructors, negative experiences in the classroom, no access to formal learning on the subject matter, and/or seeking an answer to a problem they already have.

A Learner's Time Is Valuable
Self Directed Learners dedicate their own time outside of class or work commitments to advance their knowledge. Self Directed Learners also might be specifically seeking specific information to solve a problem. In all cases, the Learner’s time is valuable.

Choice Overload
The internet and other resources provide a vast array of learning resources to refer to. Given the scale, Self Directed Learners aren’t able to find the best or most applicable resource for their needs due to a multitude of reasons, including poor search techniques or choice overload.
Research Methods
Literature Review
Literature review provided crucial early framing for the project, allowing me to gain insight into the history of formal education and the different pedagogical practices that have been popularized over the years. By understanding these practices and how they translate into the classroom, where students form their first idea of what “learning” is, I could better formulate research questions and the overall direction of the project. 

Semi-Structured Interviews
Semi-structured interviews allowed a breadth of data to be collected directly from self-directed learners. Starting with a general discussion topic and a list of overarching questions, interviewees shared their own unique experiences and their personal practices for self-directed learning. 

To prepare for semi structured interviews, I collected data on commonly used learning resources. This allowed me to easily reference services or communities that might come up during interviews and use the knowledge to probe for additional insights during interviews.

Photo Ethnography
After the topic of learning environments came up multiple times throughout my interviews, I started asking my participants if they would submit pictures of their work spaces. This allowed me to make visual connections between what participants were describing and what their typical workspace actually looked like.

Narrowing Scope & Refining HMW
Specifically for my interview process, I needed to refine my project objectives into prompts that I could form research questions from. This forced me to considered the broad nature of self study and come to the realization I needed to narrow my scope.

I chose to refine my target research audience to the creative industries. I chose the creative industries because of ease of access to research participants, their frequency engagement in self-directed learning when compared to other industries, and their usage of the digital space to engage with learning resources. By doing narrowing my research scope at this stage, I could make certain I was collecting comparable data from all participants.
⬤ What does the process of self-directed learning look like for creatives and the creative industry as a whole currently?

⬤ What are the current techniques individual self-directed learners undertake and how effective do they think they are?

⬤ Can self-directed learners self-identify conditions in which they learn best, and what are those conditions?
How might we facilitate deep learning processes in self-directed study within the creative industries?
Research Questions

⬤ What does the process of self-directed learning look like for creatives and the creative industry as a whole currently?

⬤ What are the current techniques individual self-directed learners undertake and how effective do they think they are?

⬤ Can self-directed learners self-identify conditions in which they learn best, and what are those conditions?
Analyzing Findings
The combination of literature review and user interviews excelled at providing insights into the modern self-directed learning experience. Secondary sources would detail the theoretical nature of a learning process, users would speak about it in practical terms that could be synthesized as part of a distinct experience in self-directed learning. In some cases, these experiences didn’t fit into documented learning processes and this resulted in key terminology being generated.

The addition of photo ethnography created a triangulation of research methods. Allowing me to collect findings on the theoretical nature of self-directed learning (Literature Review), the practicalities of it (Semi-structured interviews), the subconscious aspects of it (photo ethnography) and find the commonality between them.

Insights - Terminology
As more and more interviews were completed, commonality between both participants and and the language they were using allowed the creation of a common language based on the experiences and processes they had described during interviews.
Boot-Up Time
The time it takes for a Self-Directed Learner to fully immerse themselves in the study or work activity.

Workspace Nomad
A Self-Directed Learner who doesn't particularly benefit from a specific environment.

Prescriptive Learner
A learner seeking specific small scale learning resources in an effort to fill a specific knowledge gap that is blocking them from completing a learning activity. Generally the learning activity in question is externally provided in the form of a school or work assignment.

Preventative Learner
A learner seeking general knowledge or larger scale learning resources on an area of interest out of a desire to learn more and/or become skilled in it. This behavior crucially happens BEFORE an external need motivates it. The motivation for this of behavior in a learner is generally intrinsic, originating from a personal interest or a larger life / career goal.
Insights - General
A major early insight from the interviews was how easily and thoroughly each learner could discuss this topic, highlighting just how important the topic was to them.

Video Tutorials & Content
There’s a universal disdain for video tutorials in their current format. Participants described tutorials as just not fitting their needs. Popular reasons for frustration were unclear skill requirements for tutorials and the inability to scan or seek through it’s content like they might an article or a textbook

Learning Environment & Photo Ethnography
Most learners described the state of their learning environment as a key factor in whether their learning activity was effective. Being able to control the people, sounds, objectives and activities taking place in their learning environment reduced distractions, increased comfort and even helped inspire them to keep working.

Examining these insights and the story they told unveiled the journey of uncertainty that self-directed learners go through on a daily basis. Self directed learners face many challenges to their ability to learn effectively from both external and internal sources. Challenges like distracting environments and external stimuli interrupt their learning physically. While consistent frustration at their ability to locate learning resources and find value in them brings mental fatigue.

Concepts Sketches & Prototypes
After synthesizing my findings, I went to work on developing possible solutions for some of the pain points that had been uncovered. I selected individual insights and paired them up with others via both random selection and by using their associations from my previous insight sorting. By combining both of these tactics, I ensured that I would both be able generate solutions for clearly important unmet needs, as well as explore unknown possibilities and solutions. 

I explored a six concepts in low fidelity concept sketches and selected three of them to develop into medium fidelity prototypes.
Learning Mindset Quiz
For this prototype, insights were taken from the primary research interviews and organized into scales measuring mindsets and characteristics. They were then formatted into a series of questions that would help learners better identify the conditions in which they learn. The objective of this prototype was to test the viability of assisted-self evaluation in the practice of self directed learning.
Page 1 Of Mindset Quiz
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Micro Learning Design Challenges

For this prototype, a text walk through  was created. Research findings had shown learners felt anxiety and frustration when learning new workflows or software.

A controlled learning exercise would limit information overload and enable the learner to focus on building a small-scale mental model of the chosen subject. Adding a social allows learners to learn from each other and share knowledge. Consulting other learners and experts was a common behavior of learners who were building skills

Participants are challenged to complete an open ended challenge with a set time limit inside a software program, using only the tools or methods designated. Before the challenge begins, they are shown a short (2-5 minutes) demo video by a subject matter expert who displays tips and tricks using the tools provided. Participants then complete the challenge and optionally post it to social media under a provided hashtag, this gives an opportunity for both feedback and inspiration. The objective of this challenge is to quickly showcase workflows and use cases and build participants' mental models of the software while also allowing them to draw inspiration from their peers.
Interlude - Reflecting & Re-framing the Problem
I spent the period between my first and second sprint reflecting on the purpose of the project. I had cast a wide net across the problem space and had developed several prototypes that addressed some, but not all, of the needs of my participants. If all my prospective user’s problems were unique, no single packaged solution was going to be able to address every point of need in the journey of self-directed learning.

In the days I spent considering the former, I stumbled upon Helen Fisher’s research into the unique neurological activity people experience when they are in love. Dr Fisher and her colleagues had used MRI technology to discover and measure activity in a specific part of the human brain that lit up when someone was experiencing romantic love.

This prompted me to consider a completely different angle of investigation into my own problem space. Could the neurological activity that occurs during “deep learning” or “flow state” be measured? Could the circumstances and criteria needed to achieve those states be recreated and induced by the learner themselves? These are the questions that prompted my second sprint.
Part Two: Technology Exploration is on it's way! Check back soon.

Check out my other projects!

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