Title: Building personas from phenomenography: a method for user-centered design in education
Purpose Personas are lifelike characters that are driven by potential or real users’ personal goals and experiences when interacting with a product. Personas support user-centered design by focusing on real users’ needs. However, the use of personas in educational research and design requires certain adjustments from its original use in human-computer interface design. This paper aims to propose a process of creating personas from phenomenographic studies, which helps us create data-grounded personas effectively. Design/methodology/approach Personas have features that can help address design problems in educational contexts. The authors compare the use of personas with other common methodologies in education research, including phenomenology and phenomenography. Then, this study presents a six-step process of building personas using phenomenographic study as follows: articulate a design problem, collect user data, assemble phenomenographic categories, build personas, check personas and solve the design problem using personas. The authors illustrate this process with two examples, including the redesign of a professional development website and an undergraduate research program design. Findings The authors find that personas are valuable tools for educational design websites and programs. Phenomenography can productively help educational designers and researchers build sets of personas following the process the authors propose. Originality/value The use and method of personas in educational contexts are scarce and vague. Using the example contexts, the authors provide educational designers and researchers a clear method of creating personas that are relatable and applicable for their design problems. more »« less
Rees Lewis, Daniel G.; Carlson, Spencer E.; Riesbeck, Christopher K.; Gerber, Elizabeth M.; Easterday, Matthew W.(
, Journal of Engineering Education)
AbstractBackground
To create design solutions experienced engineering designers engage in expert iterative practice. Researchers find that students struggle to learn this critical engineering design practice, particularly when tackling real‐world engineering design problems.
Purpose/Hypothesis
To improve our ability to teach iteration, this study contributes (i) a new teaching approach to improve student teams' expert iterative practices, and (ii) provides support to existing frameworks—chiefly the Design Risk Framework—that predict the key metacognitive processes we should support to help students to engage in expert iterative practices in real‐world engineering design.
Design/Method
In a 3‐year design‐based research study, we developed a novel approach to teaching students to take on real‐world engineering design projects with real clients, users, and contexts to engage in expert iterative practices.
Results
Study 1 confirms that student teams struggle to engage in expert iterative practices, even when supported by problem‐based learning (PBL) coaching. Study 2 tests our novel approach, Planning‐to‐Iterate, which uses (i) templates, (ii) guiding questions to help students to define problem and solution elements, and (iii) risk checklists to help student teams to identify risks. We found that student teams using Planning‐to‐Iterate engaged in more expert iterative practices while receiving less PBL coaching.
Conclusions
This work empirically tests a design argument—a theory for a novel teaching approach—that augments PBL coaching and helps students to identify risks and engage in expert iterative practices in engineering design projects.
Kumar, Vishesh; Tissenbaum, Mike(
, British Journal of Educational Technology)
Abstract
This paper presents an implementation of Connected Spaces (CxS)—an ambient help seeking interface designed and developed for a project‐based computing classroom. We use actor network theory (ANT) to provide an underutilized posthumanist lens to understand the creation of collaborative connections in this Computational Action‐based implementation. Posthumanism offers an emerging and critical extension to sociocultural perspectives on understanding learning, by pushing us to decenter the human, and consider the active roles that human and non‐human entities play in learning environments by actively shaping each other. We analyse how students in this class adjusted their help‐seeking and collaborative habits following the introduction of CxS, a tool designed to foster (more inter‐group) collaboration. ANT proposes generalized symmetry—a principle of considering human, non‐human and more than human entities with equivalent and comparable agency, leading to describing phenomena as networks of actors in different evolving relationships with each other. Analysing collaborative interactions as fostered by CxS using an ANT approach supports design‐based research—an iterative design revision process highlighting understandings about design as well as learning—by providing a temporal and informative lens into the relationship between actors and tools within the environment. Our key findings include a framing of technologies in classrooms as bridgingagentic gapsbetween students and becoming actors engaging in different behaviours; learners enacting new agencies through technologies (for instance a more comfortable non‐intrusive help seeker), and the need for voicing and teachers to connect help networks in CxS equipped classrooms.
Practitioner notes
What is already known about this topic
Collaborative learning is a valuable skill and practice; opportunities to mentor others are critical in empowering minoritized learners, especially in STEM and computing disciplines.
School norms solidify a power and expertise hierarchy between teachers and learners and fail to productively support learners in learning from each other.
Additionally, lack of awareness about peers' knowledge is a common hindrance in students knowing who to ask for help and how.
What this paper adds
An example of a designed interface called Connected Spaces with potential to foster more inter‐student collaboration, especially outside of mandated within‐group collaboration—in the form of cross‐group help seeking and help giving.
A design based research study using actor network theory highlighting the limitations of Connected Spaces in sparking notable behaviour change among students by itself but being retooled as a teacher support tool in enabling cross‐group collaborations.
Presenting conceptions of collaboration through technologies as bridging agentic gaps and acting with new agencies in performing help‐seeking related actions.
Provoking the idea of testing emerging technologies in classrooms along with sharing our analyses and reflections with the classroom as a key idea in computing education—surfacing the gap between designed intentions and the different kinds of extra social work needed in the on‐ground success of different technologies.
Implications for practice and/or policy
Designers and researchers should create and test more interfaces alongside teachers across different classrooms and contexts aimed at supporting different kinds of voluntary collaborative interactions.
Curricula, standards and school practices should further center providing students with opportunities to engage as mentors and build communities of learning across disciplines to empower minoritized students.
Researchers engaging in design based research should consider using more posthumanist lenses to examine educational technologies and how they affect change in learning environments.
Brinkley, J.(
, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
Recent reports have suggested that most self-driving vehicle technology being developed is not currently
accessible to users with disabilities. We purport that this problem may be at least partially attributable to
knowledge gaps in practice-oriented user-centered design research. Missing, we argue, are studies that
demonstrate the practical application of user-centered design methodologies in capturing the needs of users
with disabilities in the design of automotive systems specifically. We have investigated user-centered
design, specifically the use of personas, as a methodological tool to inform the design of a self-driving
vehicle human-machine interface for blind and low vision users. We then explore the use of these derived
personas in a series of participatory design sessions involving visually impaired co-designers. Our findings
suggest that a robust, multi-method UCD process culminating with persona development may be effective
in capturing the conceptual model of persons with disabilities and informing the design of automotive system.
Kiral, Isabell; Roy, Subhrajit; Mummert, Todd; Braz, Alan; Tsay, Jason; Tang, Jianbin; Asif, Umar; Schaffter, Thomas; Mehmet, Eren; Picone, Joseph; et al(
, Challenges in Machine Learning Competitions for All (CiML))
null
(Ed.)
The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem
Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer†
* These authors contributed equally to this work
† Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com
◊
Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section
J.
Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia.
Introduction
This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating.
The challenge: enabling wide participation
With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5].
In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles:
1.
No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.)
2.
No participant should need to be an expert data scientist.
3.
No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.)
4.
No participant should need to provide their own computing resources.
In addition to the above, our platform should further
•
guide participants through the entire process from sign-up to model submission,
•
facilitate collaboration, and
•
provide instant feedback to the participants through data visualisation and intermediate online leaderboards.
The platform
The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team.
Figure 1: High-level architecture of the challenge platform
Measuring success
The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication.
Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists.
Figure 2: Primary occupation as reported by challenge participants
Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience.
Conclusion
Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.
Cherchiglia et al. Effects of ESM Use for Classroom Teams Proceedings of the Nineteenth Annual Pre-ICIS Workshop on HCI Research in MIS, Virtual Conference, December 12, 2020 1
An Exploration of the Effects of Enterprise Social Media
Use for Classroom Teams
Leticia Cherchiglia Michigan State
University leticia@msu.edu
Wietske Van Osch HEC Montreal & Michigan
State University wietske.van-osch@hec.ca
Yuyang Liang Michigan State
University liangyuy@msu.edu
Elisavet Averkiadi Michigan State
University averkiad@msu.edu
ABSTRACT
This paper explores the adoption of Microsoft Teams, a
group-based Enterprise Social Media (ESM) tool, in the
context of a hybrid Information Technology Management
undergraduate course from a large midwestern university.
With the primary goal of providing insights into the use and
design of tools for group-based educational settings, we
constructed a model to reflect our expectations that core
ESM affordances would enhance students’ perceptions of
Microsoft Teams’ functionality and efficiency, which in
turn would increase both students’ perceptions of group
productivity and students’ actual usage of Microsoft Teams
for communication purposes. In our model we used three
core ESM affordances from Treem and Leonardi (2013),
namely editability (i.e., information can be created and/or
edited after creation, usually in a collaborative fashion),
persistence (i.e., information is stored permanently), and
visibility (i.e., information is visible to other users). Analysis of quantitative (surveys, server-side; N=62) and
qualitative (interviews; N=7) data led to intriguing results.
It seems that although students considered that editability,
persistency, and visibility affordances within Microsoft
Teams were convenient functions of this ESM, problems
when working collaboratively (such as connectivity,
formatting, and searching glitches) might have prevented
considerations of this ESM as fast and user-friendly (i.e.,
efficient). Moreover, although perceived functionality and
efficiency were positively connected to group productivity,
hidden/non-intuitive communication features within this
ESM might help explain the surprising negative connection
between efficiency and usage of this ESM for the purpose
of group communication. Another explanation is that,
given the plethora of competing tools specifically designed
to afford seamless/optimal team communication, students
preferred to use more familiar tools or tools perceived as
more efficient for group communication than Microsoft
Teams, a finding consistent with findings in organizational
settings (Van Osch, Steinfield, and Balogh, 2015). Beyond theoretical contributions related to the impact that
ESM affordances have on users’ interaction perceptions,
and the impact of users’ interaction perceptions on team
and system outcomes, from a strategic and practical point
of view, our findings revealed several challenges for the
use of Microsoft Teams (and perhaps ESM at large) in
educational settings: 1) As the demand for online education
grows, collaborative tools such as Microsoft Teams should
strive to provide seamless experiences for multiple-user
access to files and messages; 2) Microsoft Teams should
improve its visual design in order to increase ease of use,
user familiarity, and intuitiveness; 3) Microsoft Teams
appears to have a high-learning curve, partially related to
the fact that some features are hidden or take extra
steps/clicks to be accessed, thus undermining their use; 4)
Team communication is a complex topic which should be
further studied because, given the choice, students will fall
upon familiar tools therefore undermining the full potential
for team collaboration through the ESM. We expect that this paper can provide insights for
educators faced with the choice for an ESM tool best-suited
for group-based classroom settings, as well as designers
interested in adapting ESMs to educational contexts, which
is a promising avenue for market expansion.
Huynh, Tra, Madsen, Adrian, McKagan, Sarah, and Sayre, Eleanor. Building personas from phenomenography: a method for user-centered design in education. Retrieved from https://par.nsf.gov/biblio/10336374. Information and Learning Sciences 122.11/12 Web. doi:10.1108/ILS-12-2020-0256.
Huynh, Tra, Madsen, Adrian, McKagan, Sarah, & Sayre, Eleanor. Building personas from phenomenography: a method for user-centered design in education. Information and Learning Sciences, 122 (11/12). Retrieved from https://par.nsf.gov/biblio/10336374. https://doi.org/10.1108/ILS-12-2020-0256
Huynh, Tra, Madsen, Adrian, McKagan, Sarah, and Sayre, Eleanor.
"Building personas from phenomenography: a method for user-centered design in education". Information and Learning Sciences 122 (11/12). Country unknown/Code not available. https://doi.org/10.1108/ILS-12-2020-0256.https://par.nsf.gov/biblio/10336374.
@article{osti_10336374,
place = {Country unknown/Code not available},
title = {Building personas from phenomenography: a method for user-centered design in education},
url = {https://par.nsf.gov/biblio/10336374},
DOI = {10.1108/ILS-12-2020-0256},
abstractNote = {Purpose Personas are lifelike characters that are driven by potential or real users’ personal goals and experiences when interacting with a product. Personas support user-centered design by focusing on real users’ needs. However, the use of personas in educational research and design requires certain adjustments from its original use in human-computer interface design. This paper aims to propose a process of creating personas from phenomenographic studies, which helps us create data-grounded personas effectively. Design/methodology/approach Personas have features that can help address design problems in educational contexts. The authors compare the use of personas with other common methodologies in education research, including phenomenology and phenomenography. Then, this study presents a six-step process of building personas using phenomenographic study as follows: articulate a design problem, collect user data, assemble phenomenographic categories, build personas, check personas and solve the design problem using personas. The authors illustrate this process with two examples, including the redesign of a professional development website and an undergraduate research program design. Findings The authors find that personas are valuable tools for educational design websites and programs. Phenomenography can productively help educational designers and researchers build sets of personas following the process the authors propose. Originality/value The use and method of personas in educational contexts are scarce and vague. Using the example contexts, the authors provide educational designers and researchers a clear method of creating personas that are relatable and applicable for their design problems.},
journal = {Information and Learning Sciences},
volume = {122},
number = {11/12},
author = {Huynh, Tra and Madsen, Adrian and McKagan, Sarah and Sayre, Eleanor},
}
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