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"note": "Justin's notes:
\n
be sure to cite and borrow from the images (different but similar).
\n
![\"\"]()
(Olmanson et al., 2021, p. 8)
\n
Justin’s Notes:
\n
\n
\n
\n
\n
\n
“ensure their alignment with real-world needs, values, and constraints, and their usefulness and usability in actual educational contexts (Bonsignore, DiSalvo, DiSalvo, & Yip, 2017; Prieto-Alvarez, Martinez-Maldonado, & Anderson, 2018; Schuler & Namioka, 1993). Yet given the complexity of learning analytics (LA) systems, it can be challenging for stakeholders to meaningfully contribute to their design (cf. Martinez-Maldonado et al., 2016; Mavrikis, Gutierrez-Santos, Geraniou, Noss, & Poulovassilis, 2013; Prieto-Alvarez et al., 2018). For example, teachers and students may have limited data literacy or limited understanding of the potentials of state-of-the-art data gathering, processing, or visualization techniques (Martinez-Maldonado et al., 2016; Mavrikis et al., 2013). Effectively co-designing LA systems with practitioners thus requires generative design techniques and strategies for eliciting end-user desires, needs, values, and constraints, untethered by specific pre-existing technologies or users’ understandings of what is currently possible. In addition, meaningfully involving non-technical stakeholders throughout the whole design process requires prototyping methods that can aid them in understanding the consequences of particular design choices in complex LA systems (e.g., how particular analytic measures, coupled with specific parameter settings and visualizations, might behave when used for different student populations), so that they can provide informed design feedback.” (Holstein et al., 2019, p. 28)
\n
“, co-design approaches can lead to unexpected technological innovations and also empower participants — giving them a voice in shaping technologies that impact their lives, and increasing the likelihood of technology acceptance and adoption (Bonsignore et al., 2017; Hoadley, 2017).” (Holstein et al., 2019, p. 28)
\n
“. Furthermore, when stakeholders are involved in the LA design process, they tend to be brought in at relatively late stages — for example, to provide feedback on existing prototypes of LA tools. Yet by this point in the design process, many fundamental design decisions have already been made, such as the pedagogical goals that a design is intended to address (Rodriguez-Triana et al., 2017; Prieto-Alvarez et al., 2018). Martinez-Maldonado et al. (2016) and Mavrikis, Gutierrez-Santos, and Poulovassilis (2013) provide case studies of LA design processes that involve frequent consultations and user testing with classroom teachers, providing rich insights into how teachers can be involved at multiple stages of the design process. In addition, recent work by Dollinger and Lodge (2018) and Prieto-Alvarez et al. (2018) has begun to synthesize high-level strategies and frameworks for involving students throughout the entirety of the design process for LA tools.” (Holstein et al., 2019, p. 29)
\n
Justin's notes
\n
In LA systems data is traditionally displayed through dashboards with indicator visualizations developed from digital footprints that learners leave when they interact with different learning environments. Indicators represent a core part of any dashboard. An indicator can be defined as “a specific calculator with corresponding visualizations, tied to a specific question” [20]. ... the current design of LA indicators is often without reference to theories in human-computer interaction (HCI) and information visualization [10,13,15,26]. ... the designed indicators are often not well aligned with user needs and expectations [5,9]. (Chatti et al., 2020)
\n
approach that aims at involving users throughout the design process. Recently, there is a growing interest in human-centered learning analytics (HCLA) as the key to user acceptance and adoption of LA systems [4,5,7,8,11,23]. (Chatti et al., 2020)
\n
how to adopt an HCLA approach that effectively involves users (i.e. learners and/or teachers) in the design of LA indicators? (Chatti et al., 2020)
\n
a variety of dashboards presenting data to various LA stakeholder groups were proposed in the LA literature [13,15,28]. (Chatti et al., 2020)
\n
Current reviews of learning dashboard research (e.g., [26,29]) tried to identify what data is presented to different LA stakeholders, why is the data presented, and how data can be presented to support sense- making processes. (Chatti et al., 2020)
\n
the design of the indicators is often without reference to theories in learning science [5,10,13,14,17,27], human-computer interaction (HCI) [15,28], and information visualization [2,25]. (Chatti et al., 2020 p.305)
\n
current indicator design solutions follow an ad hoc, data-first approach (Chatti et al., 2020 p.305)
\n
in order to design useful indicators, it is essential to (1) link the design to sound theories and models and (2) actively involve users throughout the entire indicator design process. (Chatti et al., 2020)
\n
There are four different activities in the HCD process: (1) make observations on the intended target population (Observation), (2) generate ideas (Ideation), (3) produce prototypes (Prototyping), and (4) test them (Testing). These four activities are iterated, with each cycle yielding more insights and getting closer to the desired solution. (Chatti et al., 2020)
\n
visualization use according to three questions: what data the user sees, why the user intends to use a visualization tool, and how the visual encoding and interaction idioms are constructed in terms of design choices. (Chatti et al., 2020)
\n
abstract types of what data can be visualized. The four basic dataset types are tables, networks, fields, and geometry; other possible collections of items include clusters, sets, and lists. These datasets are made up of different combinations of the five data types: items (nodes), attributes, links, positions, and grids. (Chatti et al., 2020)
\n
A task is defined as an {action, target} pair where action is a verb defining use goals and target is a noun referring to some aspect of the data that is of interest to the user. (Chatti et al., 2020)
\n
Human-Centered Indicator Design (HCID) approach borrows from the HCD and information visualization literature. It brings together Norman’s HCD process and Munzner’s what-why-how visualization framework to provide a theory-informed approach for the systematic and effective design of LA indicators. (Chatti et al., 2020)
\n
HCID enables to get the right indicator by following an HCD approach in which different stakeholders i.e. learners, teachers, designers, researchers, and developers work in close collaboration to co-design useful indicators that truly meet user needs. (Chatti et al., 2020)
\n
The HCID process, by contrast, ensures that end-users meaningfully participate at every stage of the design. Concretely, the task of designing an indicator requires the user to take four iterative stages that help develop the right indicator: (1) Define Goal/Question, (2) Ideate, (3) Prototype, and (4) Test (Chatti et al., 2020)
\n
“The purpose of visualization is insight [22]. By visualizing data, some insights emerge which might not be noticeable in the raw form of the data.” (Chatti et al., 2020, p. 304)
\n
“little attention has been paid to the systematic design of the indicators to support the intended goals.” (Chatti et al., 2020, p. 305)
\n
“the indicators are designed for and not with its users and are thus often not well aligned with user needs.” (Chatti et al., 2020, p. 305)
\n
“In most of the cases, the indicators are designed by researchers and developers and imposed on learners and teachers.” (Chatti et al., 2020, p. 305)
\n
“It is vital to engage the various LA stakeholders (learners, teachers, institutions, researchers, designers, developers, etc.) in the LA process. Especially, the learner should take an active role in the LA process, if LA tools are to serve the intended objective of improving learning [5].” (Chatti et al., 2020, p. 305)
\n
Justin’s Notes:
\n
“The field of Learning Analytics (LA) is beginning to explore new methods and strategies for co-designing LA tools with critical stakeholders such as teachers, students, and parents.” (Holstein et al., 2019, p. 27)
\n
“when implementing co-design processes, designers of LA tools should centre initial discussions on stakeholder needs, rather than specific analytics, visualizations, or other technical considerations” (Holstein et al., 2019, p. 27)
\n
“to ensure the usefulness and usability of the resulting designs, designers should scaffold stakeholders in reflecting on how particular LA displays might inform (or fail to inform) instructional decision-making and action in the context of specific tasks and scenarios.” (Holstein et al., 2019, p. 27)
\n
“Actively involving educational practitioners and other stakeholders throughout the design of educational technologies can help ensure their alignment with real-world needs, values, and constraints, and their usefulness and usability in actual educational contexts (Bonsignore, DiSalvo, DiSalvo, & Yip, 2017; Prieto-Alvarez, Martinez-Maldonado, & Anderson, 2018; Schuler & Namioka, 1993).” (Holstein et al., 2019, pp. -)
\n
“given the complexity of learning analytics (LA) systems, it can be challenging for stakeholders to meaningfully contribute to their design (cf. Martinez-Maldonado et al., 2016; Mavrikis, Gutierrez-Santos, Geraniou, Noss, & Poulovassilis, 2013; Prieto-Alvarez et al., 2018)” (Holstein et al., 2019, p. 28)
\n
“teachers and students may have limited data literacy or limited understanding of the potentials of state-of-the-art data gathering, processing, or visualization techniques (Martinez-Maldonado et al., 2016; Mavrikis et al., 2013).” (Holstein et al., 2019, p. 28)
\n
“Effectively co-designing LA systems with practitioners thus requires generative design techniques and strategies for eliciting end-user desires, needs, values, and constraints, untethered by specific pre-existing technologies or users’ understandings of what is currently possible.” (Holstein et al., 2019, p. 28)
\n
“meaningfully involving non-technical stakeholders throughout the whole design process requires prototyping methods that can aid them in understanding the consequences of particular design choices in complex LA systems (e.g., how particular analytic measures, coupled with specific parameter settings and visualizations, might behave when used for different student populations), so that they can provide informed design feedback.” (Holstein et al., 2019, p. 28)
\n
“co-design approaches can lead to unexpected technological innovations and also empower participants — giving them a voice in shaping technologies that impact their lives, and increasing the likelihood of technology acceptance and adoption (Bonsignore et al., 2017; Hoadley, 2017).” (Holstein et al., 2019, p. 28)
\n
“Furthermore, when stakeholders are involved in the LA design process, they tend to be brought in at relatively late stages — for example, to provide feedback on existing prototypes of LA tools. Yet by this point in the design process, many fundamental design decisions have already been made, such as the pedagogical goals that a design is intended to address (Rodriguez-Triana et al., 2017; Prieto-Alvarez et al., 2018).” (Holstein et al., 2019, p. 29)
\n
“Martinez-Maldonado et al. (2016) and Mavrikis, Gutierrez-Santos, and Poulovassilis (2013) provide case studies of LA design processes that involve frequent consultations and user testing with classroom teachers, providing rich insights into how teachers can be involved at multiple stages of the design process” (Holstein et al., 2019, p. 29)
\n
“recent work by Dollinger and Lodge (2018) and Prieto-Alvarez et al. (2018) has begun to synthesize high-level strategies and frameworks for involving students throughout the entirety of the design process for LA tools.” (Holstein et al., 2019, p. 29)
\n