=Paper=
{{Paper
|id=Vol-3667/DS-LAK24_paper_5
|storemode=property
|title=Automating Data Narratives in Learning Analytics Dashboards using GenAI
|pdfUrl=https://ceur-ws.org/Vol-3667/DS-LAK24_paper_5.pdf
|volume=Vol-3667
|authors=Adriano Pinargote,Eddy Calderón,Kevin Cevallos,Gladys Carrillo,Katherine Chiluiza,Vanessa Echeverria
|dblpUrl=https://dblp.org/rec/conf/lak/PinargoteCCCCE24
}}
==Automating Data Narratives in Learning Analytics Dashboards using GenAI==
https://ceur-ws.org/Vol-3667/DS-LAK24_paper_5.pdf
Automating Data Narratives in Learning Analytics
Dashboards using GenAI
Adriano Pinargote1,∗ , Eddy Calderón1 , Kevin Cevallos1 , Gladys Carrillo1 ,
Katherine Chiluiza1 and Vanessa Echeverria1,2
1
Escuela Superior Politecnica del Litoral , (Information Technology Center), Campus Gustavo Galindo Km. 30.5 Vía
Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
2
Monash University, Clayton, VIC, Australia
Abstract
This paper presents an innovative approach leveraging Generative Artificial Intelligence to automate
data narratives within Learning Analytics Dashboards for collaborative learning scenarios. Focusing
on the analysis of class meeting transcripts, the study delves into specific collaboration skill metrics,
transforming raw data into a cohesive narrative. Validation through inter-rater reliability, utilizing
Cohen’s Kappa coefficient, establishes the reliability of both human and AI assessments. The integration of
Large Language Models, such as ChatGPT3.5, is explored, shedding light on their potential in educational
narrative assessment. The proposed methodology not only enhances understanding of class dynamics but
also contributes a practical tool for educators, seamlessly translating raw data into visually compelling
narratives. The paper concludes with insights from a pilot test, revealing student perceptions and
addressing concerns around AI impact on dashboard utility and fairness. This research advances the
intersection of data storytelling and Learning Analytics Dashboards, offering valuable insights into
collaborative learning dynamics.
Keywords
Dashboards, Narrative Storytelling, Artificial Intelligence, GenAI
1. Introduction
The convergence of data storytelling and narratives within Learning Analytics Dashboards
(LADs) has gained significant attention due to its potential to convey insights to non-expert
audiences [1]. The use of data storytelling and narratives in education provides effective
communication, personalized learning experiences, reflective opportunities, informed decision-
making, and performance improvement for both students and educators. Researchers and
educational stakeholders often collaborate using human-centered design approaches to align
pedagogical intentions with data insights [2, 3]. While the importance of these narratives is
Joint Proceedings of LAK 2024 Workshops, co-located with the 14th International Conference on Learning Analytics and
Knowledge (LAK 2024), Kyoto, Japan, March 18-22, 2024.
∗
Corresponding author.
Envelope-Open adriano.pinargote@cti.espol.edu.ec (A. Pinargote); eddy.calderon@cti.espol.edu.ec (E. Calderón);
kevin.cevallos@cti.espol.edu.ec (K. Cevallos); gladys.carrillo@cti.espol.edu.ec (G. Carrillo); kchilui@espol.edu.ec
(K. Chiluiza); vanessa.echeverria@monash.edu (V. Echeverria)
Orcid 0009−0001−8123−2684 (A. Pinargote); 0009-0004-7417-3294 (E. Calderón); 0009-0003-8581-6200 (K. Cevallos);
0000-0002-9142-6482 (G. Carrillo); 0000-0001-5992-6236 (K. Chiluiza); 0000−0002−2022−9588 (V. Echeverria)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
recognized in past research [4, 5], the manual generation of insights remains a time-consuming
challenge. Inspired by recent exploits of Generative Artificial Intelligence (GenAI) in natural
language processing [6], this paper introduces an approach for automatically generating data
narratives applied in educational contexts [7]. Our approach includes segmenting behavioural
data, refining GPT-3.5 comprehension prompts through prompt engineering, validating GenAI
outputs, and generating a dashboard. We illustrate our approach in a pilot study with participants
(students) to initially explore its usefulness, understanding and fairness in representing the data.
2. Related Works and Research Gaps
2.1. Learning Analytics Dashboards and Narratives
LADs have been widely used over the past ten years [8] as they provide a comprehensive visual
overview of teacher and student performance [9] aiming at closing the feedback loop [10] and
support timely interventions. Learning Analytics Dashboards (LADs) have the potential to
communicate crucial insights to educators, administrators, and students, fostering informed
decision-making [8]. Integrating diverse metrics, LADs provide a snapshot of academic progress,
identifying patterns and trends that impact student outcomes. From tracking classroom engage-
ment to assessing task performance, LADs are essential for targeted interventions, aiming to
enhance education quality and offer personalized learning experiences [9]. However, limitations
persist, including challenges in interpreting complex data and visualizations by teachers and
students [11] and a gap in aligning pedagogical needs with the data [12].
In recent years, Learning Analytics (LA) researchers and designers have embraced InfoVis and
visualization design principles for communicating insights to a non-expert audience [13]. Data
storytelling (DS) is the art of conveying insights and information through a narrative-driven
presentation of data. Several works incorporate data storytelling principles into LAD design,
aiming to communicate key insights to teachers and students. This approach supports the
interpretation of critical data insights, facilitating reflection and behavior adaptation for future
practices [10]. Prior works have established a structured process of converting conventional
visualizations into visualisations with data storytelling elements [14, 5, 15]. These dashboards
aim not only to present extensive data but also to make information meaningful by delivering
“one story at a time” [16] and utilizing narratives for user feedback [17]. This approach simplifies
information and highlights users’ key points in visualizations.
A notable gap in current research focuses on feedback mechanisms and narrative elements
within LADs. While prior studies have acknowledged the potential impact of DS elements
(i.e., narratives) in LADs for teaching and learning practices [14, 5, 15], there is a lack of
approaches that generate these DS elements, especially if the aim is to scale the generation of
such dashboards.
2.2. Generative AI (GenAI) in Education
The fundamental concept behind Generative AI lies in training models on extensive datasets,
empowering them to generate original content that closely mimics human language patterns[18].
In the realm of education, there remains limited knowledge on how to optimize researchers’
collaborative experiences with Generative AI.
According to [19], educational stakeholders can enhance their LADs by performing data
processing and generation tasks through LLMs. Nevertheless, there exist some challenges and
opportunities, such as privacy, fairness, ethics, accessibility concerns, and the inherent challenge
of how to customize the LADs supported by GenAI to be more “learner” oriented. Another
potential use of GenAI is in thematic analysis, where such systems offer a heightened level of
user autonomy through natural language (NL) interaction, which differs from previous machine-
learning and pattern-based systems [6] and the inclusion of NL processing by comparing LLMs
such as GPT3 and ChatGPT to present reliable and accurate information to any audience [20] and
the automatization of creating images, charts, and even maps [21] with a few NL instructions.
A systematic review [22] highlighted ChatGPT’s potential in healthcare research and education
for performing thematic analysis tasks but emphasized the essential requirement for robust
guidelines to address potential misuse and also a robust validation of the resulting coding data.
These two examples show the potential to empower researchers using GenAI for the automatic
identification of behaviors. However, to our knowledge, there is a lack of approaches addressing
the challenge of identifying behaviors from learning data using GenAI. Particularly, researchers
should consider the alignment of pedagogical intentions (i.e., expected or salient behaviors in
learning environments) with the data. In addition, given the capabilities of GenAI to extract
insights and patterns from large amounts of data [19], it is valuable for LA researchers to
recognize the feasibility of GenAI to automatically generate narratives of behavioural data.
These GenAI narratives can uncover patterns that may be hidden due to the large amounts of
data humans need to analyze and synthesize.
3. An Approach to Generating Data Narratives using GenAI
This section outlines our approach to data narrative generation within a LAD. Our goal is to
translate the pedagogical intentions of the learning activity into narrative elements, emphasizing
specific data aspects and providing personalized recommendations to students. The approach
(Figure 1) comprises the following components:
Figure 1: The proposed approach to automatically translate pedagogical intentions into data narratives.
A) Context: It is essential to understand the context of the learning environment. In our
approach, this context is represented as the key pedagogical intentions that will be
rendered in the dashboard. These pedagogical intentions can be derived from theory (e.g.,
[23]), through inquiry methods (e.g., [14]) or by using more structured approaches (e.g.,
[1]). These pedagogical intentions can be represented as codes or metrics to which we
wish to draw’s attention in the dashboard.
B) Behavioral Data: Our focus is to analyze behavioural data gathered from educational
contexts. Here, we consider any form of data that captures human-human or human-
machine interaction and that is collected through audio, video or any other device. Ideally,
the goal of researchers is to code this data to reveal interaction patterns that could help
understand the key pedagogical intentions in the learning context. This data could be
represented as a transcript containing the timestamps and the content of such interactions.
C) Automatic Coding: Our aim is to feed the LLM using the pedagogical intentions (metrics)
and the behavioural data (transcript) to automatically extract patterns that could be then
translated into insights in the dashboard. We implemented a prompt, following the
guidelines outlined in the OpenAI manual [24]. Adopting these techniques involves
crafting an initial prompt, executing it using the OpenAI API, and subsequently reviewing
the output to determine its correctness. This revision is an iterative process until a human
(i.e., an observer or researcher) considers the output consistent and contains the desired
information.
In this component, we leverage GPT 3.5 to automatically code the data using the
key pedagogical intentions (metrics). In a prompt, we specify the i) topic, ii) data,
iii) instruction, and iv) outcome. In the topic, we describe an overview of the context,
including the goal and background of the data and the pedagogical intentions (e.g., “These
are the collaborative aspects that participants should demonstrate during a collaborative
activity.” ). The data is the transcript split into smaller chunks to adjust to the technical
specifications of GPT 3.5 (the limited use of tokens). In the instruction, we describe the
analysis we need for this task, which is content coding (e.g., “Consider the data from X
student. Code each utterance by identifying if the utterance contains coordination aspects.” ).
Finally, in the outcome, we specify the desired output format (e.g., JSON) that can be
used by other applications (e.g., Vue.js) or in further analysis.
D) Data Narratives Generator: In this component, we leverage GPT 3.5 to generate
insights from the data. In a prompt, we specify the i) topic, ii) instruction, and iii)
outcome. In the topic, similar to the previous component, we describe an overview
of the context. We also describe an overview of the data (i.e., type, source, summary,
transformations, etc) we have previously performed (as in C). In the instruction, we
describe the analysis we need to generate the narratives (e.g., “Write a summary of the
behaviors each participant adopted during a collaborative activity.” ). Finally, similar to the
previous component, we specify the desired outcome (e.g. JSON file with the narratives)
that other applications will use.
Next, we illustrate our approach in the context of online collaborative learning.
4. Methods
4.1. Learning Activity and Collaboration Aspects
In the Object Oriented Programming subject, students often need to participate in collaborative
activities. Students are allocated in groups (3 students per group) to solve an activity. Each
group selects one of three proposed applications, which have already been curated by the
teacher. Then, together, as a group, students should generate a final document specifying the
objects needed to deploy the application, a description of the objects and a summary describing
the most challenging object to be implemented.
From prior works, we developed six metrics [25, 23] to evaluate students’ collaboration
aspects exhibited during the activity, namely communication, mutual support, coordinating,
work environment, commitment, and problem management. In addition, we also included on-
topic/off-topic tasks to understand if students are more focused when working in the activity.
Table 1 presents the description of these metrics. These metrics and descriptions will be used
as the pedagogical intentions, as described in our approach (Figure 1 - A)
4.2. Data Collection and Processing
Three researchers participated in the collaborative activity, as described above. They were asked
to initiate a Teams meeting, which was video and audio recorded using an automated script.
The meeting transcript, generated through Wishper 1 , included timestamps and text for each
participant’s utterance, treated as the unit of analysis. A total of 494 utterances were identified
for analysis (Figure 1 - B).
Following our approach, we automatically coded (Figure 1 - C). Meeting transcription snippets
from Teams were sent to GPT 3.5. They were segmented into snippets of 900 words or less
(approximately 2000 tokens) and sent to GPT 3.5. Once the entire transcript was processed,
the prompt call categorized each utterance into collaboration metrics and on-topic/off-topic
categories.
4.3. Coding Utterances into Collaboration Metrics
Each utterance was coded into one or several metrics, meaning that multiple collaborative
aspects can appear within a single utterance. In addition, each utterance was also categorized
into on-topic/off-topic. We performed a human validation with three raters and employed
Krippendorff’s Alpha and Cohen’s Kappa to assess the inter-rater reliability. This validation
was carried out in two parts:
• Human Coding:
Three coders independently coded each utterance. The inter-rater reliability analysis
required a collective commitment of 165 hours from three human observers —50, 55, and
60 hours each— to categorize utterances according to the specified metrics. At the end of
this task, the coders participated in a discussion session to reach a consensus on the final
code per each utterance. A threshold of 0.61 indicates a substantial agreement between
1
https://openai.com/research/whisper
Table 1
Inter-rater reliability Results
3 coders (Krip- Human Vs GenIA
Metric Description
pendorff) (Kappa)
On Topic Comments relevant to the main topic 0.72 0.661
Off Topic Comments unrelated to the main topic 0.681 0.680
Effectiveness of verbal communication
Communication 0.507 0.557
among team members
Mutual Support Assisting when facing challenges 0.734 0.749
Coordination Team’s ability to work together efficiently 0.802 0.872
Work Environ- Creates a conducive and inclusive work
0.443 0.790
ment environment within the team.
Measures team members’ dedication and
Commitment 0.503 0.728
engagement towards shared goals
Problem Manage- Effectively manage disagreements and
0.558 0.668
ment problems during meeting
raters. As listed in Table 1, coders achieved a substantial agreement in on-topic/off-topic
coordination and mutual support. In contrast, a moderate agreement was achieved in the
rest of the metrics.
• GenAI: The previously mentioned prompt call was employed for the automatic coding
of each utterance. Notably, the GenAI accomplished this task within 5 minutes. We
computed the agreement between human coding and GenAI results, utilizing Cohen’s
Kappa. Remarkably, the GenAI demonstrated substantial agreement in on-topic/off-topic
categories and all collaboration metrics, except for Communication, where moderate
agreement was observed (Kappa=0.557).
4.4. Data Narratives
From the data resulting in the automatic coding, we calculated the corresponding percentages
for each collaboration aspect per group and per individual. This information was used in the
data narratives generator (Figure 1 - D). We generated 1) a summary of the activity, describing
the main insight of the meeting and the role of each student during the activity; 2) a group
summary, describing the overall group’s behaviors concerning the collaboration metrics; and
3) individual feedback, describing the main insight and areas of improvement per each student
according to the collaboration metrics.
4.5. Dashboard Prototype
Based on the outputs from the automatic coding and data narratives (Figure 1 - C & D), a
high-fidelity dashboard prototype was developed using Vue.js. The web application receives
two JSON files to generate the charts. The dashboard has three main elements, as illustrated in
Figure 2.
Figure 2: GenAI-powered dashboard: A) Meeting summary, B) Metrics’ Charts and C) Metrics’ Feedback
The dashboard displays graphs and text detailing participants’ assessments for each metric,
using distinctive colors for clarity. The primary goal is to provide an overview of the meeting,
identifying prominent roles and areas for improvement. It starts with a general summary
(Figure 2A), followed by detailed individual and group metrics (Figure 2B). This approach offers
a comprehensive view, enabling students and teachers to identify areas for improvement and
aspects needing consolidation and strengthening (Figure 2C) based on the six metrics.
5. Pilot Study with Students
5.1. Participants and Procedure
A pilot study with third-year bachelor students from computer science and mechatronics
undergraduate programs was conducted to explore the usefulness, understanding, fairness
and perceived impact of the dashboard. 19 students (3 female) participated in this pilot study.
Students were exposed to the dashboard, similar to the example in Figure 2. The task consisted
of 1) exploring each part of the dashboard (A, B and C) using a think-aloud protocol and 2)
answering Likert-scale (1-5; 1 being the lowest value and 5 being the highest value), yes/no and
open-ended questions based on their perceptions. Six questions (Q# represents the question’
number) were focused on gathering perceptions (usefulness, understanding, fairness) from the
LAD. The students did not know the LAD was generated using GenAI. After each Likert scale
and yes/no questions, students were asked to elaborate on their responses.
Table 2
Interview Questions and Responses (19 participants)
Q# LAD Section Type Mean Median Min Max Std Dev
1 Metrics’ Charts Usefulness 4.42 4.00 3.00 5.00 0.61
2 Metrics’ Feedback Usefulness 4.32 5.00 3.00 5.00 0.82
3 Summary Understanding 4.32 5.00 3.00 5.00 0.82
4 Summary Usefulness 4.32 5.00 1.00 5.00 1.00
# Yes NO Question YES NO
5 Metrics’ Charts & Feedback Fairness 13 6
6 Summary Fairness 12 7
5.2. Preliminary Analysis and Results
For the Likert scale questions, we calculated basic statistics (mean, median, min, max, std. dev.).
For the open-ended questions, we searched for quotes that could help us understand the values
and challenges when exploring the dashboard. Table 2 summarizes the findings of the pilot
study. Students’ responses (S# represents students’ comments) were gathered according to their
experience with the LAD:
• Metrics’ Charts and Feedback: In terms of usefulness, students indicated a high
perception on these chart and narratives in the metrics chart (Q1) (Figure 2-B). Students
appreciated the insights gained from charts, suggesting a valuable tool for enhancing
their understanding of collaborative aspects. S10 mentioned: “The metrics of what each
participant truly contributed are valuable to me and how I can improve in the following
meetings.”. Regarding the metrics’ feedback (Q2), it has a wider standard deviation (0.82),
this means that students found it useful but some may hold a diverse perspective on the
usefulness of this feedback (Figure 2C). S2 said: “I believe they (metrics’ feedback) are
redundant because if I already have a percentage (metric chart), the textual summary can
be inferred.” In fairness (Q5), most students (65%) perceived that the metrics’ feedback
are fair, because they recognize each student contribution, S16 says: “They (metrics’
feedback) not states non-participation but ensures to present the qualities and both strengths
and weaknesses.”, but S12 (and 35%) opposes: “I don’t perceive them (metrics’ feedback) as
directly related to the collaboration metrics percentages (Figure 2B). It would even be unfair
to compare students in this context.”
• Summary: Students generally found the summary (Figure 2A) easy to understand (Q3)
because they were able to identify their roles. They also valued that the summary
highlighted what had been discussed at the meeting. S5 expressed:“Because it can provide
self-feedback, I can reflect on the roles I play in a team. I realized that sometimes I assume a
particular role frequently.”. However, in terms of its usefulness (Q4), while most students
agreed that this summary and it’s narrative is useful, one student perceived the opposite –
affecting the standard deviation. This student explained that: “If the (summary) feedback
is provided to us by the teacher (in-person), it has more significance.”. In terms of fairness
(Q6), most of the students (65%) agreed that the general summary fairly describes the
participation of all students. As expressed by S16: “Capturing the entire context of the
activity is crucial. It doesn’t imply a lack of participation; rather, it ensures a comprehensive
presentation of qualities, strengths, and weaknesses.”. In contrast, 35% expressed that the
information provided is not fair. S12 remarked: “Assigning roles (to students) might seem
unfair, someone could feel uncomfortable being consistently confined into a specific role.”
6. Discussion and Future Work
In the challenge of moving from raw data to an interpretation of results, the approach followed
in this paper resembles prior works [26], where data from meeting recordings were utilized to
create a dashboard employing social network analysis, providing team members with insights
into their communication behavior. Our approach uses the same idea but differs by automating
the process of generating insights using GenAI [19]. Through our approach, we effectively
analyzed the quality of collaboration by measuring collaborative aspects from the transcript,
reducing time and use of human resources. Preliminary responses from students show promising
results about the potential value of these automatic process, but also highlight the challenges
of using GenAI, in particular, when discussing the fairness of representation. Researchers
are invited to investigate ethics, reliability, trustworthiness, and safe principles inherent to
intelligent LAD systems [27].
One challenge of this research was to handle a substantial volume of data extracted from
human-human and human-computer interactions, in our case, through speech transcripts. One
transcript can contain 50 pages of text, which is a large amount of data for an LLM (i.e., GPT
3.5). Effectively processing this large dataset presented difficulty in achieving accurate results,
given that the more data, the more difficult it becomes for model to understand the whole
context. Our solution uses various Prompt Engineering techniques [24] and sheds light on the
automatic generation of narratives that communicate insights, a challenge highlighted in prior
LAD literature[8]. However, this approach opens up new research venues and opportunities to
address issues such as prompt and data quality validation, inviting researchers to investigate
and develop human-AI collaboration approaches to overcome these challenges [18].
AI denotes remarkable efficiency in recognizing patterns [18], completing assessments in
minutes compared to humans, highlighting its potential for streamlining evaluations, but the
lack of transparency in the “blackbox” process is remarkable as it is a closed-source system;
the inappropriate use of LLMs could raise legitimate concerns about data privacy [28], which
brings us to the issue of security [29], security risks in ChatGPT commonly include command
injection, data poisoning, privacy leaks, malicious content generation and ethical risks related
to bias, highlighting the need for robust security and ethical measures in its development and
use, bearing in mind that this management [30] of the information is extremely isolated from
the user.
Choosing Cohen’s Kappa and Krippendorf for this study allowed us to highlight the ability to
measure inter-rater agreement robustly [31], the substantial agreement among human observers
and moderate to substantial agreement with AI assessments suggest reliability convergence,
challenging conventions and emphasizing the need for collaborative human-AI evaluation in
education [6, 18]; the inclusion of AI would be sooner a “normal” path to be taken to potentiate
the education field; however, there is the need of validation until a reliable standard is reached.
7. Conclusion
This article introduces an automated approach employing GenAI for the creation of data narra-
tives in the context of collaborative learning through the use of LADs. The research showcases
the potential of AI in streamlining evaluation processes and underscores the significance of a
collaborative synergy between human and AI evaluators. Additionally, it highlights the crucial
role of exploring and utilizing various tools within the LLM environment for achieving precise
outcomes. The overall implications of the findings suggest that the integration of data story-
telling and narratives into LADs holds promise for educational stakeholders and non-expert
audiences, offering valuable insights and improving comprehension of collaborative endeavors.
References
[1] G. M. Fernandez-Nieto, R. Martinez-Maldonado, V. Echeverria, K. Kitto, D. Gašević, S. Buck-
ingham Shum, Data storytelling editor: A teacher-centred tool for customising learning
analytics dashboard narratives, in: LAK24: 14th International Learning Analytics and
Knowledge Conference, 2024.
[2] D. Wang, H. Han, Applying learning analytics dashboards based on process-oriented
feedback to improve students’ learning effectiveness, British Journal of Educational
Technology 51 (2020) 555–569.
[3] V. Echeverria, R. Martinez-Maldonado, R. Granda, K. Chiluiza, C. Conati, S. Bucking-
ham Shum, Driving data storytelling from learning design, in: Proceedings of the 8th
International Conference on Learning Analytics and Knowledge, LAK ’18, Association for
Computing Machinery, New York, NY, USA, 2018, p. 131–140.
[4] B. Dykes, Data storytelling: What it is and how it can be used to effectively communicate
analysis results, 2015.
[5] R. Martinez-Maldonado, V. Echeverria, G. Fernandez, S. Buckingham Shum, From data
to insights: A layered storytelling approach for multimodal learning analytics, 2020, pp.
1–15. doi:10.1145/3313831.3376148 .
[6] J. V. Pavlik, Collaborating with chatgpt: Considering the implications of generative artificial
intelligence for journalism and media education, Journalism & Mass Communication
Educator 78 (2023) 84–93.
[7] C. K. Y. Chan, W. Hu, Students’ voices on generative ai: perceptions, benefits, and
challenges in higher education, International Journal of Educational Technology in Higher
Education 20 (2023) 43.
[8] I. Jivet, M. Scheffel, H. Drachsler, M. Specht, Awareness is not enough: Pitfalls of learning
analytics dashboards in the educational practice, in: É. Lavoué, H. Drachsler, K. Verbert,
J. Broisin, M. Pérez-Sanagustín (Eds.), Data Driven Approaches in Digital Education,
Springer International Publishing, Cham, 2017, pp. 82–96.
[9] W. Matcha, N. A. Uzir, D. Gašević, A. Pardo, A systematic review of empirical studies on
learning analytics dashboards: A self-regulated learning perspective, IEEE Transactions
on Learning Technologies 13 (2020) 226–245.
[10] W. Greller, H. Drachsler, Translating learning into numbers: A generic framework for
learning analytics, Educational Technology Society 15 (2012) 42–57.
[11] L. Corrin, Evaluating Students’ Interpretation of Feedback in Interactive Dashboards, 2018,
pp. 145–159.
[12] K. Verbert, X. Ochoa, R. D. Croon, R. A. Dourado, T. D. Laet, Learning analytics dashboards:
the past, the present and the future, Proceedings of the Tenth International Conference on
Learning Analytics & Knowledge (2020).
[13] C. N. Knaflic, Storytelling with data: A data visualization guide for business professionals
(12 ed.), Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of
Mathematical Sciences], John Wiley Sons, New Jersey, USA, 2017.
[14] V. Echeverria, R. Martinez-Maldonado, S. Buckingham Shum, K. Chiluiza, R. Granda,
C. Conati, Exploratory versus explanatory visual learning analytics: Driving teachers’
attention through educational data storytelling, Journal of Learning Analytics 5 (2018).
[15] S. Pozdniakov, R. Martinez-Maldonado, Y.-S. Tsai, V. Echeverria, N. Srivastava, D. Gasevic,
How do teachers use dashboards enhanced with data storytelling elements according to
their data visualisation literacy skills?, in: LAK23: 13th International Learning Analytics
and Knowledge Conference, LAK2023, New York, NY, USA, 2023, p. 89–99.
[16] G. Fernandez-Nieto, V. Echeverria, S. Buckingham Shum, K. Mangaroska, K. Kitto,
E. Palominos, C. Axisa, R. Martinez-Maldonado, Storytelling with learner data: Guiding
student reflection on multimodal team data, IEEE Transactions on Learning Technologies
PP (2021) 1–1.
[17] G. Fernandez Nieto, K. Kitto, S. Buckingham Shum, R. Martinez-Maldonado, Beyond
the learning analytics dashboard: alternative ways to communicate student data insights
combining visualisation, narrative and storytelling, in: LAK 2022 Conference Proceedings,
USA, 2022, pp. 219–229.
[18] L. Yan, V. Echeverria, G. F. Nieto, Y. Jin, Z. Swiecki, L. Zhao, D. Gašević, R. Martinez-
Maldonado, Human-ai collaboration in thematic analysis using chatgpt: A user study and
design recommendations, 2023.
[19] L. Yan, R. Martinez-Maldonado, D. Gasevic, Generative artificial intelligence in learning
analytics: Contextualising opportunities and challenges through the learning analytics
cycle, 2023.
[20] P. Maddigan, T. Susnjak, Chat2vis: Generating data visualisations via natural language
using chatgpt, codex and gpt-3 large language models, 2023. doi:10.48550/arXiv.2302.
02094 .
[21] R. Tao, J. Xu, Mapping with chatgpt, ISPRS International Journal of Geo-Information 12
(2023). URL: https://www.mdpi.com/2220-9964/12/7/284. doi:10.3390/ijgi12070284 .
[22] M. Sallam, Chatgpt utility in healthcare education, research, and practice: Systematic
review on the promising perspectives and valid concerns, Healthcare 11 (2023).
[23] A. Meier, H. Spada, N. Rummel, A rating scheme for assessing the quality of computer-
supported collaboration processes, International Journal of Computer-Supported Collabo-
rative Learning 2 (2007) 63–86.
[24] OpenAI, Openai, six strategies for getting better results, https://platform.openai.com/docs/
guides/prompt-engineering/six-strategies-for-getting-better-results, 2023.
[25] V. Echeverria, P. Garaizar, J. Garcia-Zubia, Dimensions-an exploratory evaluation of a
collaboration feedback report, in: Proceedings of the 12th International Conference on
Learning Analytics and Knowledge (LAK22), 2022, pp. 480–484.
[26] T. Spielhofer, R. Motschnig, Developing teams by visualizing their communication struc-
tures in online meetings, Multimodal Technologies and Interaction 7 (2023).
[27] R. Alfredo, V. Echeverria, Y. Jin, Z. Swiecki, D. Gasevic, R. Martinez-Maldonado, Slade: A
method for designing human-centred learning analytics systems, 2024.
[28] M. Gupta, C. Akiri, K. Aryal, E. Parker, L. Praharaj, From chatgpt to threatgpt: Impact of
generative ai in cybersecurity and privacy, IEEE Access 11 (2023) 80218–80245. doi:10.
1109/ACCESS.2023.3300381 .
[29] X. Wu, R. Duan, J. Ni, Unveiling security, privacy, and ethical concerns of chatgpt, Journal of
Information and Intelligence (2023). URL: https://www.sciencedirect.com/science/article/
pii/S2949715923000707. doi:https://doi.org/10.1016/j.jiixd.2023.10.007 .
[30] L. Ayinde, M. P. Wibowo, B. Ravuri, F. B. Emdad, Chatgpt as an important tool in
organizational management: A review of the literature, Business Information Re-
view 40 (2023) 137–149. URL: https://doi.org/10.1177/02663821231187991. doi:10.1177/
02663821231187991 . arXiv:https://doi.org/10.1177/02663821231187991 .
[31] Z. Xiao, X. Yuan, Q. V. Liao, R. Abdelghani, P.-Y. Oudeyer, Supporting qualitative analysis
with large language models: Combining codebook with gpt-3 for deductive coding, in:
28th International Conference on Intelligent User Interfaces, ACM, 2023, pp. 1–4.