# Generative Artificial Intelligence in Learning Analytics: Contextualising
Opportunities and Challenges through the Learning Analytics Cycle

Lixiang Yan  Monash UniversityAustralia ,  Roberto Martinez-Maldonado  Monash
UniversityAustralia and  Dragan Gašević  Monash UniversityAustralia

(2024)

###### Abstract.

Generative artificial intelligence (GenAI), exemplified by ChatGPT,
Midjourney, and other state-of-the-art large language models and diffusion
models, holds significant potential for transforming education and enhancing
human productivity. While the prevalence of GenAI in education has motivated
numerous research initiatives, integrating these technologies within the
learning analytics (LA) cycle and their implications for practical
interventions remain underexplored. This paper delves into the prospective
opportunities and challenges GenAI poses for advancing LA. We present a
concise overview of the current GenAI landscape and contextualise its
potential roles within Clow’s generic framework of the LA cycle. We posit that
GenAI can play pivotal roles in analysing unstructured data, generating
synthetic learner data, enriching multimodal learner interactions, advancing
interactive and explanatory analytics, and facilitating personalisation and
adaptive interventions. As the lines blur between learners and GenAI tools, a
renewed understanding of learners is needed. Future research can delve deep
into frameworks and methodologies that advocate for human-AI collaboration.
The LA community can play a pivotal role in capturing data about human and AI
contributions and exploring how they can collaborate most effectively. As LA
advances, it is essential to consider the pedagogical implications and broader
socioeconomic impact of GenAI for ensuring an inclusive future.

learning analytics, human-AI collaboration, generative artificial
intelligence, ChatGPT, Midjourney, educational technology

††copyright: acmcopyright††journalyear: 2024††doi: XXXXXXX.XXXXXXX††ccs:
Applied computing Collaborative learning††ccs: Applied computing Computer-
assisted instruction††ccs: Applied computing Learning management systems

##  1\. Introduction

Generative artificial intelligence (GenAI), catalysed by the public release of
ChatGPT in November 2022, has sparked a new era of innovation and
productivity, particularly in the educational sector. Tools like ChatGPT,
powered by state-of-the-art large language models (LLMs), have illustrated
their potential in reshaping the educational landscape, fostering enhanced
engagement and interactive learning experiences (Van Dis et al., 2023; Kasneci
et al., 2023). A comprehensive scoping review has identified 53 different
applications of LLMs in automating educational tasks (Yan et al., 2023).
Subsequent studies delving into ChatGPT and other LLM tools illustrated their
capabilities in delivering comprehensive feedback, surpassing the efficacy of
human educators in articulating student performance (Dai et al., 2023). These
models also outperformed average students in reflective writing tasks (Li et
al., 2023b) and innovated conversational assessments that could potentially
transform conventional digital formative assessments (Yildirim-Erbasli and
Bulut, 2023). Yet, the spotlight on ChatGPT represents just the tip of the
iceberg in the vast GenAI ecosystem. The maturity of text-to-image GenAI
models like DALL-E 2 and Midjourney has facilitated the creation of dynamic
teaching resources, supporting multimedia learning in specialised fields such
as medical education (Mazzoli et al., 2023) and craft training (Vartiainen and
Tedre, 2023). Meanwhile, speech-to-text models, such as OpenAI’s Whisper,
could potentially transform educational content transcription (Rao, 2023) and
automate the capture of collaborative discourse (Cao et al., 2023). The GenAI
landscape further broadens with text-to-code models like OpenAI’s Codex and
text-to-audio models like Meta’s Voicebox (Le et al., 2023), offering learning
analytics (LA) researchers and practitioners a diverse toolkit for data
analytics and communication. As these tools continue to evolve, their
integration into the LA cycle (Clow, 2012) holds promise in addressing some of
the pressing challenges in the field, especially the lack of attempts to
intervene in the learning environment (Motz et al., 2023). Nevertheless, how
these novel technologies can be embedded in the LA cycle (Clow, 2012) and
benefit the development of practical LA solutions remains largely unknown.
This position paper explores the opportunities and challenges GenAI poses for
advancing learning analytics. The contribution of this work to the LA
community is that we contextualised the opportunities and challenges of GenAI
within Clow’s (Clow, 2012) LA cycle and summarised six endeavours for future
research. These contributions could pave the way for future LA research and
practice to integrate GenAI tools effectively, ensuring that they align with
educational goals and uphold the principles of inclusive education while
navigating the ethical complexities introduced by these advanced technologies.

##  2\. Current Landscape of Generative AI

We first provide a concise overview of the current landscape of GenAI,
focusing on the state-of-the-art models, promising applications, and
supporting infrastructures instead of the underlying algorithms or
technological mechanisms, as the aim of this paper is to explore the practical
implications of GenAI in LA. While LLMs like GPT-4 and Llama-2 have attracted
most of the attention in academic research, especially in education (Van Dis
et al., 2023; Kasneci et al., 2023; Choi et al., 2023), GenAI’s capabilities
extend beyond merely text-to-text generation but also to other modalities,
such as image, video, and speech generation (Chiu, 2023; Mazzoli et al., 2023;
Rao, 2023). Within text-based GenAI, there are many application scenarios
beyond chatbots like ChatGPT. For example, the idea and exploration into AI
agents (e.g., AgentGPT (Reworkd, 2023)) were based on GPT-4. These AI agents
are autonomous and adaptive AI that work towards a goal and make decisions
independently without continuous user input. They could potentially self-
generate prompts based on the outputs of the previous prompt, making them a
potential candidate for working proactively with humans (Hauptman et al.,
2023) and simulating human behaviours (Park et al., 2023). Similar to text
generation, code generation also utilises LLMs but with additional training on
code-related content (e.g., OpenAI’s Codex and Meta’s Code Llama). These text-
to-code generation models excel when paired with a code interpreter like in
ChatGPT-4, where they can automatically conduct data analysis on provided
datasets and with natural language prompts (Wang et al., 2023a).

On the other hand, image generation models like Midjourney, Stable Diffusion,
and Dall-E are capable of generating high-quality images from natural language
text prompts. While these text-to-image generation models have been previously
criticised for their inconsistency and lack of controllability over multiple
generations, which might limit the practical utility in creating narrated or
specialised contents (e.g., illustration of cardiac arrest training) (Mazzoli
et al., 2023; Chiu, 2023), recent progress, such as Midjourney’s new
’inpainting’ function and the latest Dall-E 3 have made them more controllable
and consistent for image generation tasks. Likewise, video generation models
like Runway’s Gen-2 and Meta’s Make-A-Video (Singer et al., 2022) are capable
of creating video from text, image, or a combination of both. However, the
practical utility of this line of GenAI models still needs to be discovered as
the technologies become more mature and accessible. Speech-wise, text-to-
speech models like Meta’s Voicebox (Le et al., 2023) are capable of generating
and synthesising audio that mirrors the tone and voice of a human in multiple
different languages. Whereas, speech-to-text models like OpenAI’s Whisper
(Gris et al., 2023) have demonstrated their ability to automatically generate
transcripts from audio inputs with performance comparable and even surpass
human transcription when the audio is severally influenced by background noise
(Gris et al., 2023). Preliminary studies that utilised Whisper to
automatically transcribe educational videos have already shown promising
results in enriching the modality of existing educational resources (Rao,
2023).

Apart from these foundational generative models, there are also many
supporting infrastructures that have been deployed to address some of their
challenges. In particular, for LLMs, the emergence of vector embedding
databases like Milvus (milvus.io) aims to address the limited context length
of most existing models (e.g., GPT-3.5/4 and Llama 2). This limitation is
often imposed on these LLMs to optimise computational resources as the self-
attention mechanism, which is core to Transformer models, struggles with very
long sequences due to its quadratic complexity (Vaswani et al., 2017). Vector
embedding is the representation of objects, such as words or entities, as
vectors in a continuous vector space to capture semantic or relational meaning
(Mikolov et al., 2013). Such embedding databases can also act as an external
knowledge database for LLMs, where they can draw knowledge from and use these
databases to limit the context of AI responses. For example, a common workflow
for developing in-document questions and response systems involves using an
application framework (e.g., Langchain and Llama Index) to segment the
document content into different chucks, generate the vector embedding of each
chuck, and store them in an embedding database, which can be retrieved and
compared with the embedding of prompts to identify the most relevant chunks.
Together, the prompt and these identified chucks can be sent to LLMs like
GPT-4 to generate contextually accurate and reliable responses (Cui et al.,
2023).

##  3\. Generative AI in Learning Analytics

Clow’s (Clow, 2012) generic LA cycle provides a well-grounded framework for
exploring the potential influence of GenAI in LA research and practices as it
highlights the four essential steps in LA, including identifying the learner,
collecting the relevant data, generating and presenting analytics, and
delivering and evaluating interventions. As shown in figure 1, GenAI have
multiple implications for each of these steps. The following sections
elaborated on each of these steps and discussed the relevant theoretical and
empirical basis.

Figure 1. Contextualising the implications of generative artificial
intelligence within each step of Clow’s learning analytics cycle.

###  3.1. Learner

The foundation of LA is centred on understanding learning and learners
(Gašević et al., 2015). A pivotal initial step in the LA cycle is identifying
the learner and the contexts in which learning occurs. This step is essential
for ensuring that LA aligns with its primary objective: to enhance learning
for learners in learning environments (Lang et al., [n. d.]). Before the
prevalence of GenAI, pinpointing the learner was relatively straightforward.
Typically, a learner could be an individual pursuing a degree or education at
a university or in school, or participating in a Massive Open Online Course
(MOOC), other online education platforms, or even physical space. Under such
circumstances, LA researchers and practitioners could confidently ascertain
the learner’s identity and their learning environment. Given this clarity, the
data collected, either from the learning products (e.g., assessments) or the
learning processes (e.g., interactions in online forums), can be deemed
reliable to a certain degree for modelling learners’ metacognitive (e.g.,
self-regulated learning (Roll and Winne, 2015; Winne, 2022) and socially
shared regulation of learning (Järvelä et al., 2015, 2023b)), cognitive (e.g.,
memory and reflection (Kovanović et al., 2018; Gibson et al., 2017)), and
affective processes (e.g., emotion and motivation (Suero Montero and Suhonen,
2014; D’Mello and Jensen, 2022)).

As GenAI becomes more capable, the underlying assumption of knowing learners’
identity might face challenges. GenAI tools like ChatGPT and Claude, which
students can utilise for various learning tasks, have blurred the boundary
between learners and tools. These tools have been demonstrated to generate
learning products indicative of high metacognitive, cognitive, and social
skills. Specifically, recent research indicates that ChatGPT can produce
reflective writings that surpass the quality produced by average undergraduate
and graduate pharmacy students. Notably, these AI-generated writings were
indistinguishable from student submissions by both educators and state-of-the-
art AI detection systems (e.g., OpenAI’s AI text classier) (Li et al., 2023b).
Such capabilities also extend to structured and high-stake assessments.
Preliminary findings suggest that GenAI tools, like GPT-3 and ChatGPT,
successfully passed the United States Medical Licensing Exam (Kung et al.,
2023), passed four real exams at the University of Minnesota Law School (Choi
et al., 2023), and answered basic operations management and process-analysis
questions from Wharton School of Business examinations (Terwiesch, 2023).
While these test-taking performances might be expected considering potential
data contamination, where the vast training data of these LLMs already
contained the answers, there is also evidence showing signs of emotional
intelligence in GenAI. A study highlighted that AI-generated responses to
patient queries in an online forum were deemed more empathetic and of higher
quality than responses from physicians by experts (Ayers et al., 2023).

Given GenAI’s capabilities, the products of learning, such as written
assignments and test scores, can easily be influenced. On the other hand, the
processes of learning, which include the steps, strategies, and cognitive
activities learners engage in, may remain more resistant to GenAI
interference. While GenAI can provide learners with information or even
automate certain tasks, the actual cognitive processes, emotions, and
motivations that constitute their learning process are still human-centric
(Kasneci et al., 2023; Järvelä et al., 2023a). However, as GenAI become more
sophisticated, there is also the potential for it to not only complete
learning products but also mimic certain learning processes. For instance, a
student might employ an AI tool to read and summarise content, bypassing the
cognitive process of comprehension. While this might lead to higher task
efficiency, it raises questions about the depth and authenticity of their
learning process and challenges the very essence of what it means to be a
’learner’ (Rudolph et al., 2023).

Therefore, instead of solely focusing on validating learners’ identities, the
LA community should pivot towards understanding the evolving nature of the
learner in the age of GenAI. Learners will increasingly be expected to
interact with and utilise GenAI tools in their professional capacities
(Kasneci et al., 2023). This calls for a shift in our conceptualisation of
learners. Drawing from novel insights and frameworks like hybrid human-AI
regulation of learning (Molenaar, 2022), artificial and human cognition
(Siemens et al., 2022), human and AI collaboration in socially shared
regulation of learning (Järvelä et al., 2023a), hybrid intelligence (Järvelä
et al., 2023c), and human-AI teaming (Zhang et al., 2021), we advocate for a
renewed understanding of learners. Rather than seeing GenAI as a threat to
learning integrity, we should view it as an opportunity to transform learning
in the context of human-AI collaboration (Joksimovic et al., 2023; National
Academies of Sciences, Engineering, and Medicine and others, 2021). This
evolving learner definition has significant implications for other stages of
the LA cycle, offering exciting avenues for both theoretical and empirical
research. The LA community can play a pivotal role in capturing data about
human and AI contributions and exploring how they can collaborate most
effectively.

###  3.2. Data

LA is a data-centred field that leverages learners’ data to understand and
optimise their learning process (Lang et al., [n. d.]). Yet, not all data is
relevant. The second step of the LA cycle is to capture relevant data for
understanding and improving learners’ learning process (Clow, 2012). These
data typically involve survey data (e.g., self-report measures of learner
perceptions and self-efficacy), trace data (e.g., digital traces in online
learning platforms like clicks and page views), and text data (e.g., forum
posts, discourse in intelligence tutoring system, and written assessments)
(Gray and Bergner, 2022). Beyond these conventional data streams, recent
advancements in multimodal LA and game LA also highlighted the educational
value contained in learners’ physical and physiological traces (e.g., gesture,
speech, and heart rate) (Ochoa, 2022), and game progression, achievements, and
decisions (Reardon et al., 2022). In terms of capturing relevant learner data,
while GenAI could potentially introduce complications and novel challenges,
such as attributing learner agency, it can also open up new opportunities for
transforming and augmenting existing data-capturing processes in LA, including
formatting unstructured text data, expanding the modality of learner data, and
support the generating of synthetic data for model training and open-science
initiatives. The following sections elaborate on each of these challenges and
opportunities.

####  3.2.1. Learner Agency

As the boundaries between AI and human learners blur, the significance of
capturing data about the learning processes and accounting for learner agency
in LA becomes even more pronounced (Jääskelä et al., 2021). Learner agency is
defined as learners’ capacity to act independently and make decisions
regarding their learning (Bandura, 1989). For LA to optimise learning for
human learners, it is essential to understand their agency during the learning
process as it determines what they choose to learn and how they conduct
learning, navigate challenges, and integrate feedback (Reeve, 2013). GenAI
poses a significant challenge in identifying when and how a learner exerts
agency over their learning process as learners may offload or collaborate with
GenAI to conduct various learning tasks (Rudolph et al., 2023). For example,
some students may use GenAI tools to improve the grammar and presentation of
their reflective writing, while others may ask these tools to generate the
entire reflective assessment, undermining the educational purpose of such
tasks. LA should ideally be able to capture and distinguish such differences
in learner agency as these differences also contain significant insights. For
example, based on the social cognitive perspective of self-regulation
(Zimmerman, 2000), a learner’s decision to use GenAI tools for specific tasks
but not others could potentially offer insights into their self-efficacy,
values, and strategic decision-making processes. Likewise, from established
work in goal orientation and self-regulated learning (e.g., traced goal
orientation (Zhou and Winne, 2012)), learners’ interactions with these GenAI
tools can also provide some insights into their metacognitive processes, such
as when they seek GenAI for assistance, when they disagree, challenge, or
question AI-generated results, and how they integrate AI-generated content
with their unique understanding and perspectives. As GenAI becomes
increasingly prevalent, the failure to capture learner agency could lead to
biased and unfair interpretations of learning data and conceal the rich
interplay of decision-making, motivation, and self-regulation (Turner and
Patrick, 2008). Given this context, the LA community must urgently build upon
the established frameworks (e.g., Winne and Hadwin’s COPES model (Winne and
Hadwin, 1998)) and emerging research on human-AI regulation (Molenaar, 2022;
Järvelä et al., 2023a) and student agency analytics (Jääskelä et al., 2021;
Hooshyar et al., 2023) to devise robust measures and mechanisms that capture
learner agency (Fan et al., 2022). This ensures that the essence of learning
remains deeply rooted in human-centric principles.

####  3.2.2. Automated Transcription

GenAI could also support multimodal LA solutions that relied on capturing and
analysing learners’ verbal communications (Chua et al., 2019). A major
challenge exists in the accuracy and timeliness of transcribing learners’
audio data as this process was often done in a post-hoc manner and mostly
relied on manual efforts or external services, limiting the potential of these
analytics in supporting real-time feedback and reflection practices (Yan et
al., 2022). GenAI tools like Whisper and other speech-to-text models have
demonstrated the potential to automate the transcription process (Rao, 2023),
opening up the opportunity to streamline the process of capturing and
transcribing audio data to text data, which can be processed by established LA
models or LLMs to extract meaningful insights, such as reflecting different
teamwork constructs (e.g., situation awareness and close-loop communication)
(Zhao et al., 2023). While the research on the validity and reliability of
GenAI in transcribing speech data still requires further empirical
validations, as this line of research matures, it could potentially offer
future LA research a less labour-intensive and integrated approach to cover
meaningful insights within learners’ verbal communication data.

####  3.2.3. Dynamic Representations

The diversity of learner data that may become available for future LA research
may also be enriched by the prevalence of GenAI. Specifically, GenAI’s
capabilities in generating human-like multimodal content introduce novel
opportunities to enrich the modality and interactivity of the data that can be
used in LA research and practices, offering a more comprehensive understanding
of the learner’s learning process (Chiu, 2023; Mazzoli et al., 2023; Lee et
al., 2023). Central to this potential is GenAI’s capability to generate
dynamic representations of learning (Zhang and Linn, 2011). Instead of the
conventional reliance on static text or images, learners can now harness GenAI
tools to transform their textual reflections or visual illustrations into
animated sequences. For example, text-to-image models like Stable Diffusion,
Midjourney, and Dall-E (Radford et al., 2021; Chiu, 2023) can empower
educators and learners to convert textual descriptions into detailed visual
content. Such advancements could progress LA to diverse learning settings,
such as art-focused STEAM education (Lee et al., 2023), where learners’
continued interactions with these tools (e.g., prompts and iterations) may
provide the data required to gain a deeper understanding of learners’
cognitive processes like their proficiency in translating abstract concepts
into tangible products (Fiorella and Kuhlmann, 2020; Zhang and Linn, 2011).
Likewise, the emergence of text-to-video generation tools, such as Runway
Gen-2 and Make-A-Video (Singer et al., 2022), further enhance the richness of
data available for analysis in LA. By transforming textual content into
engaging video sequences, these tools can enhance the immersive nature of
learning environments, shedding light on learners’ reasoning and narrative
abilities. Such dynamic representations are especially pivotal for capturing
reliable data on learners with specific disabilities, like dyslexia (Long et
al., 2007), reinforcing LA’ commitment to inclusive education (Slee, 2018).
However, capturing learner agency during these novel learning processes is
essential for ensuring the efficacy of LA.

####  3.2.4. Synthetic Data

Another opportunity that GenAI may contribute to the future of LA regarding
data is supporting synthetic data initiatives. In LA research, synthetic data
can serve as a valuable intermediary while waiting for actual data to become
available, especially during bureaucratic processes like gaining approval from
multiple ethics boards or navigating data ownership issues (Berg et al.,
2016). Such synthetic data can offer researchers immediate access, especially
those who may not have access to different data sources, enabling them to
refine methodologies and evaluate the scalability and adaptivity of their
solutions in data-rich environments. In a closely related field, educational
data mining, synthetic data generation are already gaining momentum in data-
mining contexts (Rahman et al., 2022), suggesting that a similar approach can
be extended to LA (Berg et al., 2016). For example, synthetic data can
potentially support the development of privacy-preserving LA (Gursoy et al.,
2016), provide LA training materials (Koester, 2015), examine the inner
workings of knowledge tracing models (Flanagan et al., 2022), and contribute
to the open-science practices in LA research (Dorodchi et al., 2019). While
most of the previous studies aimed to generate synthetic data that mirrored
the statistical attributes of learner data (e.g., demographic, educational
data (Dorodchi et al., 2019)), prior works on simulated learners illustrated
its potential in playing the role of a tutoring training system for educators,
learning partners for students, test fields for instructional designers to
conduct formative evaluations with different learning systems (Flanagan et
al., 2022; McCalla and Champaign, 2013). However, these simulated learners
were often limited in their ability to mimic actual human learners due to the
complexity of human metacognition, cognition, and affection (Flanagan et al.,
2022). GenAI could potentially address these issues, especially with the
recent emergence of AI agents, which are autonomous and adaptive AI that work
towards a goal without continuous user input (e.g., AgentGPT (Reworkd, 2023)).
For example, these AI agents have also been used to simulate human behaviour
at a scale of 25 agents, where they were able to engage in daily interaction,
organise parties, and make new friends (Park et al., 2023). Considering the
versatility of these GenAI models, using these AI agents to play the role of
learners, educators, and specialised roles, such as standard patients for
medical students’ diagnosis practice training (Song et al., 2022), could open
up a range of novel learning opportunities, and thus, generating rich data for
future LA research to tackle.

###  3.3. Analytics

The third step of the LA cycle involves extracting analytics from learner data
to generate meaningful insights into the learning process. These analytics can
be categorised into four different types (SOLAR, 2021), including descriptive
analytics that use data aggregation and visualisation to understand historical
trends and patterns of learning behaviours, diagnostic analytics that use data
mining and correlation analysis to understand the reasons behind observed
trends and relationships, predictive analytics that focus on combining
historical data with statistical models to forecast potential trends and
behaviours, and prescriptive analytics that aim to recommend possible courses
of action based on a combination of machine learning algorithms, business
rules, and computational modelling. Apart from extracting these analytics from
learner data, adequate reporting and presentation in LA dashboards are also
essential to ensure their utility and impactfulness (Verbert et al., 2020;
Jivet et al., 2018; Matcha et al., 2019). Human-centred LA approaches are
essential for designing effective LA that can be adopted, comprehended, and
used successfully to support learning and teaching practices (Buckingham Shum
et al., 2019; Tsai and Martinez-Maldonado, 2022). We foresee four major
potentials for GenAI to enhance the analysis, presentation, and
informativeness of LA and diversify the interactions that educational
stakeholders could potentially have with future LA dashboards. These
potentials include supporting the analysis of unstructured data, enabling the
multimodal illustration of LA, enriching the informativeness of LA with
elaborated explanations, and contributing to the development of interactive
LA. The following subsection elaborated on each of these potentials.

####  3.3.1. Unstructured Data Analysis

Using natural language processing techniques to analyse and extract insights
from unstructured text data is a rapidly evolving focus of LA research,
spanning across educational discourse modelling (Dowell and Kovanović, 2022)
and writing analytics (Gibson and Shibani, 2022; Allen et al., 2022). These
data directly reflect the learners’ experience, perceptions, and linguistic
behaviours during learning tasks. They can provide different and potentially
more enriched insights into their learning process and learning gains (Gray
and Bergner, 2022). However, transforming unstructured text data to structured
metrics that can be digested by LA systems to extract meaningful indicators is
challenging. While conventional natural language processing techniques, such
as Linguistic Inquiry and Word Count and Latent Dirichlet Allocation (Allen et
al., 2022; Xing et al., 2020), enabled opportunities to process unstructured
text data, their utility is often limited to a specific task like extracting
linguistic features or topic modelling, potentially limiting the practical
values of the resulting insights. Prior studies using conventional natural
language processing techniques for human-AI collaboration in qualitative
analysis have identified limitations regarding the ability of these solutions
to deal with the intricacies in the linguistic and semantic features of
different language structures (Gebreegziabher et al., 2023; Rietz and Maedche,
2021). On the contrary, GenAI, specifically LLMs, may offer more diversity and
scalability in processing and analysing unstructured text data. For example, a
study that used LLMs (GPT-3) for deductive coding found that with a pre-
determined codebook, the model has achieved fair to substantial agreements
with expert-coded results (Xiao et al., 2023). Furthermore, a recent study
illustrated that state-of-the-art GenAI tools like ChatGPT demonstrated
superior capabilities in capturing a variety of language structures. The
ability of ChatGPT to elaborate on its decision was also deemed helpful to
improve construct validity, pinpoint potential ambiguity in code definitions,
and support human coders to achieve higher inter-rater reliability than prior
tools (e.g., nCoder) (Zambrano et al., 2023). This approach of using GenAI to
explain its own decisions could potentially enhance the transparency of the
decision-making process or at least provide additional information to cross-
validate with humans. However, the validity and reliability of GenAI in
analysing unstructured text data still require further investigation.

####  3.3.2. Multimodal Illustration

As discussed in Section 3.2.3, GenAI can potentially enhance the dynamic
representations of learning resources and learner data. Similar opportunities
may also apply to the illustration and presentation of LA. Prior LA research
has demonstrated the benefits of data-driven personalised feedback in
improving learners’ academic achievement (Pardo et al., 2019; Lim et al.,
2021). One of these systems, OnTask (Pardo et al., 2018), offers an interface
that allows educators to transform learner data into specific rules for email
generation, thereby enabling personalised feedback for each learner and
ensuring that the feedback is directly relevant to their learning progress.
For such personalised feedback, GenAI offers the opportunity to move beyond
mere text or graphical feedback but also enables the generation of audio and
potential video feedback. For example, using 3D diffusion model (Wang et al.,
2023b) and text-to-speech models (e.g., Meta’s Voicebox (Le et al., 2023)),
educators can generate a digital representation of themselves and deliver the
feedback content through narrated voice instead of merely text. Apart from
benefiting learners with dyslexia (Long et al., 2007) or preferring multimedia
learning (Mayer, 2005), there is also some evidence showing that audio and
video feedback was perceived as more personal, fostering learner
comprehension, and more dynamic and engaging than written feedback (McCarthy,
2015; Orlando, 2016). Yet, the lack of usage of these feedback modalities was
mainly associated with the workload and technical challenges in creation and
distribution (McCarthy, 2015), which can be potentially addressed with the
advancement in GenAI and the emergence of low-tech solutions. However, a
cautious approach must be taken when using these ’deepfake’ technologies to
generate personable content as they present unique ethical and cybersecurity
challenges (e.g., identity theft and fake information) (Westerlund, 2019).

####  3.3.3. Explanatory Analytics

Besides supporting dynamic representations, GenAI can also augment existing LA
visualisations by providing elaborated explanations. This potential resonates
with the emerging studies that utilised data storytelling to enhance learners’
and educators’ interpretation of LA (Martinez-Maldonado et al., 2020; Nieto et
al., 2022). For example, prior studies have illustrated that adding text
narrative and a layered approach to LA visualisations has provoked deeper
reflection in educators (Martinez-Maldonado et al., 2020) and supports the
sensemaking of complex visualisations (Nieto et al., 2022). Moving away from
exploratory and towards explanatory visual LA could facilitate learners’ and
educators’ ease of interpretation and provide more actionable insights
(Echeverria et al., 2018). However, a current limitation in this line of
research is the dependency on structured learning activity, which is required
to formulate the rules and protocols that generate the explanations. This
limitation undermines the utility of explanatory analytics in open-ended
learning tasks where learning behaviours were less predictable (Martinez-
Maldonado et al., 2020). GenAI could potentially contribute to addressing such
limitations. One potential approach is to use GenAI (e.g., AgentGPT (Reworkd,
2023)) to create simulated learners and run multiple learning simulations, in
which clustering techniques and reinforcement learning can be applied to
identify prevalent learning strategies (Flanagan et al., 2022; McCalla and
Champaign, 2013). These simulated learning strategies can derive rules and
protocols for supporting explanatory analytics in open-ended learning tasks.
Another potentially more generalisable approach is to use retrieval-augmented
generation (Lewis et al., 2020) with LLMs to generate more accurate and
context-sensitive explanations. This approach could potentially be achievable
by storing previous knowledge of learning tasks (e.g., learning design and
theoretical underpinnings) in an embedding database (Ham et al., 2020), where
LLMs can draw on for more contextual information when generating explanations
for visual LA (visualisations can be generated automatically by LLMs with a
code interpreter plugin like ChatGPT-4).

####  3.3.4. Interactive Analytics

GenAI can also help to address several essential issues around LA dashboards,
including one-size-does-not-fit-all, actionability, and data literacy. To
address these issues, Verbert et al. (Verbert et al., 2020) emphasised the
need to move beyond building LA dashboards that serve merely as a one-way
information route, where users receive information and potentially get more
explanations through interaction (e.g., click on a data point). GenAI could
transform these conventional dashboards into dynamic, conversational systems,
where LA dashboards become interactive platforms that foster the collaboration
between the user and the dashboard in achieving a shared goal like enhancing
learning outcomes (Charleer et al., 2017). For example, using retrieval-
augmented generation (Lewis et al., 2020) to connect GenAI with course
resources and relevant theoretical frameworks (e.g., self-regulated learning
(Winne, 2022; Matcha et al., 2019)) could support AI-powered dashboards to
proactively recommend resources, interventions, or learning strategies to
learners based on their current performance and trajectory instead of merely
presenting static data. By infusing relevant contextual and theoretical
knowledge into AI-powered dashboards, LA dashboards can also become more
interactive, where users can engage in conversation with the dashboards (e.g.,
a chatbot for the dashboard) to enhance their interpretation and
internalisation of the analytics (e.g., provoking self-reflection and planning
behaviours (Jivet et al., 2017)). However, ensuring that AI-powered dashboards
respect data privacy (e.g., transparency in data usage) and avoid potential
inaccuracy and biases in their communication and recommendations is crucial
(Verbert et al., 2020).

###  3.4. Interventions

The final step to complete the LA cycle is going beyond methodologies,
instruments, and scientific results and applying LA in practice to deliver
interventions that optimise the learning process and outcomes (Clow, 2012).
Such interventions could be providing adequate support to students who have
been identified as at-risk through predictive LA (Herodotou et al., 2019) or
utilising multimodal LA to reflect and improve learners’ oral presentation
skills (Ochoa et al., 2018). While this final step is essential to make LA
more impactful and contribute to addressing immediate and long-standing
challenges in education (Gašević et al., 2015; Wise et al., 2021), it is also
the step that is most underdeveloped in existing LA literature (Papamitsiou et
al., 2020) with merely a small amount of studies that demonstrated practical
benefits on learning outcomes, supports, and teaching (Viberg et al., 2018).
Regarding supporting LA intervention initiatives, we foresee three major
opportunities that GenAI may offer to promote such interventions’ development,
delivery, and evaluation. These opportunities include enhancing the level of
personalisation available through LA interventions, improving the adaptivity
of these interventions from a longitudinal perspective, and broadening the
accessibility of LA across different learners and regions of the world. The
following sections elaborate on these opportunities and discuss the associated
challenges.

####  3.4.1. Personalisation

Prior studies on learners’ attitudes and expectations of LA have consistently
identified personalisation as one of the expected values of LA (Pardo et al.,
2019; Schumacher and Ifenthaler, 2018; Roberts et al., 2017). For example,
learners desired LA dashboards that generate personalised feedback and
recommendations to support further improvements (Ochoa et al., 2018; Karaoglan
Yilmaz and Yilmaz, 2020). However, personalisation is a deeply human-centred
approach, where different learners may have diverse perspectives. For example,
some learners may feel uncomfortable and unfair for others to receive
additional learning content or support as they could potentially be
disadvantaged during the process of personalisation, others may prefer
automated and unidentifiable notifications over personalised and identifiable
emails from educators (Roberts et al., 2017; Pardo et al., 2018). Likewise,
personalisation is also associated with dynamic representations (Section
3.2.3) and multimodal illustration (Section 3.3.2), as different learners may
have different preferences over the reporting and presentation of LA
(McCarthy, 2015; Orlando, 2016). These differences in individual preferences
pose challenges in developing LA interventions that could cater to the
requirements of different learner groups. To achieve such in-depth levels of
personalisation, LA interventions should be developed based on the needs,
knowledge, performances, learning process and contexts of individual learners
(Buckingham Shum et al., 2019; Roberts et al., 2017). While this level of
personalisation seems almost impossible to achieve, we foresee the potential
to move towards this goal by utilising the aforementioned capabilities of
GenAI. For example, the ability of GenAI to transform unstructured text into
structured data (Xiao et al., 2023), analyse diverse linguistic structures
(Zambrano et al., 2023), and generate coherent feedback (Dai et al., 2023) can
be integrated into functions that foster the reciprocal communications between
learners and LA interventions (Verbert et al., 2020), where these
communications (e.g. chat history) can be processed to update learners’
current knowledge, performances, and learning process and contexts and
informing researchers and practices about any emerging needs in a real-time
manner (e.g., automatically summarised report). Additionally, combined with
GenAI’s ability to generate multimodal illustrations, learners could control
what, where, and how they would like to receive LA interventions. However,
this take on personalisation falls into the philosophical debate of individual
autonomy (Hannafin and Land, 1997) versus paternalism (Dworkin, 2014), which
demands LA researchers and practitioners to consider the balance between
structured guidance and learner autonomy over personalisation for different
learner groups (e.g., age and culture).

####  3.4.2. Adaptive Intervention

Another opportunity that GenAI may bring to the delivery of LA involves its
capabilities to support adaptive intervention. Effective LA and technologies
should provide learners with adaptive support tailored to their current
knowledge, performances, learning process and contexts (Han et al., 2021;
Forsyth et al., 2016). Prior LA research has focused on creating learner
profiles that capture the essence of a learner’s history, preferences, and
performance metrics to tailor educational experiences (Corrin et al., 2017;
Zamecnik et al., 2022; Barthakur et al., 2023). These profiles, based on
demographics and accumulated trace data over time, have been instrumental in
informing researchers and educators about learners’ learning behaviours (e.g.,
help-seeking (Corrin et al., 2017)) and providing adequate support to optimise
their educational experiences (Barthakur et al., 2023). We foresee an exciting
opportunity for integrating GenAI into prior LA research on learner profiles
(Corrin et al., 2017; Zamecnik et al., 2022; Barthakur et al., 2023). GenAI’s
ability to analyse vast and diverse datasets (as discussed in Section 3.3.1)
allows it to construct detailed learner profiles, taking into account various
competencies, attributes, and experiences (e.g., captured through interactive
analytics; Section 3.3.4). Unlike conventional rule-based or static models,
which may often be confined to specific disciplinary domains, GenAI can
integrate data across multiple domains, offering a more holistic view of a
learner’s learning trajectory. Additionally, by connecting GenAI with external
knowledge (through multiple embedding databases), it could potentially
navigate the complexities of various educational spaces (e.g., technical,
social, and political (Barthakur et al., 2023)), offering insights that are
not just confined to individual contexts but span across multiple educational
scenarios. This comprehensive profiling can aid in bridging the gap between
current assessment practices and a more adaptive, learner-centred approach
(Hannafin and Land, 1997). The potential of GenAI also lies in its ability to
be integrated with previously validated methodologies and instruments. For
example, recent system infrastructural progress of the FLoRA engine (a LA
platform for supporting self-regulated learning) (Li et al., 2023a) has
combined learners’ prior knowledge and self-regulated learning process to
generate adaptive prompts for ChatGPT to provide learners with adaptive
scaffolding during their learning process. Such integration could advance
future LA interventions, making them more adaptive and holistic.

####  3.4.3. Accessibility

Regarding accessibility, GenAI presents a nuanced scenario for future LA
interventions. While its capabilities in dynamic representations could indeed
be beneficial for learners with specific challenges or disabilities, such as
dyslexia (Long et al., 2007), there is an imminent concern that the economic
implications might overshadow these advantages (Van Dis et al., 2023). For
those individuals and institutions with financial capacities, leveraging
commercial GenAI tools or deploying open-source versions can potentially
enhance the depth, interactivity, and multimodality of their LA interventions,
as elaborated in Sections 3.3.2 and 3.3.4. However, the computational
resources associated with the development, deployment, and operation of GenAI
are concerning, potentially mirroring or even exceeding their capabilities
(Van Dis et al., 2023; Kasneci et al., 2023). Such economic barriers could
widen the digital divide, placing advanced GenAI-powered LA tools out of reach
for many learners, especially those in under-resourced settings (Pontual
Falcão et al., 2020). Likewise, while GenAI could potentially support
personalised learning pathways, it can amplify the looming risk of
disadvantaging large numbers of people and creating a two-tiered education
system: one where affluent learners enjoy personalised, dynamic, and
interactive learning experiences, and another where less privileged learners
are stuck with static, one-size-fits-all content (Selwyn, 2019). The ethical
implications of this discrepancy are significant. It raises questions about
equity in education and the potential for GenAI and LA to heighten existing
inequalities rather than alleviate them (Yan et al., 2022; Slee, 2018).
Additionally, while the initial investment in GenAI might be high, there is
also the ongoing cost of maintenance, updates, and ensuring compatibility with
educational methodologies and policies (Tsai and Gasevic, 2017). As LA
advances, it is crucial to consider the pedagogical implications of GenAI and
its broader socioeconomic impact. A balanced approach, where the benefits of
GenAI are made available more equitably while mitigating its challenges, will
be essential for the future of inclusive and effective LA interventions (Slee,
2018).

##  4\. Future Research Endeavours with Generative AI

We summarised the contextualised opportunities and challenges of GenAI through
the LA cycle (Clow, 2012) into six potential research endeavours for future
studies to explore and investigate the synergy between LA and GenAI. Through
these endeavours, the LA community can effectively and ethically leverage the
technological advancement of GenAI and fulfil its foundational purpose of
optimising learning for all learners.

Fist Endeavour: Hybrid Human-AI Learning Paradigms. From the learner aspect,
as the lines blur between learners and GenAI tools, future LA research should
delve deep into frameworks and methodologies that advocate for a collaborative
learning experience between humans and AI. Investigating these hybrid models
will offer in-depth insights into learners and their learning process in the
age of GenAI (Järvelä et al., 2023a, c; Zhang et al., 2021; National Academies
of Sciences, Engineering, and Medicine and others, 2021).

Second Endeavour: Advanced Analysis of Unstructured Learning Data. From the
data aspect, given the richness of unstructured text data in reflecting
learners’ cognitive processes, future LA research should focus on leveraging
GenAI for a nuanced analysis of such data. This would involve processing
discourse, reflections, and other forms of textual data to gain deeper
insights into the learning process (Dowell and Kovanović, 2022; Gibson and
Shibani, 2022; Xiao et al., 2023; Zambrano et al., 2023).

Third Endeavour: Enhanced Visual and Explanatory Analytics. From the analytics
aspect, GenAI can augment conventional LA dashboards and visualisations,
offering more detailed, contextual explanations and data storytelling. Future
research should look at integrating GenAI to make LA more actionable,
interactive, and user-friendly, ensuring a deeper understanding and reflection
for both learners and educators. (Martinez-Maldonado et al., 2020; Nieto et
al., 2022; Charleer et al., 2017; Jivet et al., 2017).

Forth Endeavour: Deep Personalisation and Adaptive Interventions. From the
intervention aspect, with GenAI’s capability to offer real-time updates and
create detailed learner profiles, future LA interventions can achieve deeper
personalisation. This requires research into how GenAI can be integrated to
ensure interventions remain relevant and effective throughout a learner’s
educational journey (Buckingham Shum et al., 2019; Roberts et al., 2017; Han
et al., 2021; Barthakur et al., 2023).

Fifth Endeavour: Enhancing Accessibility in LA with GenAI. From the
intervention aspect, future research should ensure that advancements in GenAI-
powered interventions are accessible to all learners, regardless of their
socio-economic background. This requires addressing economic barriers and
ensuring that LA interventions remain inclusive and equitable (Long et al.,
2007; Slee, 2018; Yan et al., 2022).

Sixth Endeavour: Ethical and Equitable Integration of GenAI in LA. Overall, as
GenAI becomes more embedded in LA, addressing ethical concerns becomes
essential. Future research should focus on ensuring the integration of GenAI
in LA respects data privacy, maintains ethical standards, and does not amplify
existing educational inequalities and problematic practices, such as gender
bias and discrimination (Tsai et al., 2020; Slee, 2018; Yan et al., 2022;
Verbert et al., 2020).

##  5\. Final Remark

For LA to achieve its promise in optimising learning, the LA community needs
to venture beyond merely uncovering insights about learning or developing
innovative methodologies and analytics. The promise of LA lies in its
actionable application—addressing real-world educational problems and
benefiting both teaching and learning processes (Mazzoli et al., 2023; Wise et
al., 2021; Papamitsiou et al., 2020). This position piece illustrates a
promising synergy between GenAI and LA. Such a combination has the potential
to support systemic changes in education, making LA more comprehensive,
evidence-based, and aligned with the real-world complexities of education
(Tsai and Gasevic, 2017). Yet, despite this optimistic vision, we must be
vigilant. GenAI also introduces new theoretical complications, such as
redefining the very essence of a ’learner’, and has boarder socioeconomic
impacts, notably the risk of widening the digital divide. As the field of LA
moves forward, these considerations must be at the forefront of a collective
endeavour among LA researchers, practitioners, and policymakers, ensuring an
inclusive future for both LA and education in the era of GenAI.

###### Acknowledgements.

This research was at least in part funded by the Australian Research Council
(DP210100060) and Jacobs Foundation (Research Fellowship).

## References

  * (1)
  * Allen et al. (2022) Laura K. Allen, Sarah C. Creer, and Püren Öncel. 2022\.  Natural Language Processing as a Tool for Learning Analytics - Towards a Multi-Dimensional View of the Learning Process.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 46–53.
  * Ayers et al. (2023) John W Ayers, Adam Poliak, Mark Dredze, Eric C Leas, Zechariah Zhu, Jessica B Kelley, Dennis J Faix, Aaron M Goodman, Christopher A Longhurst, Michael Hogarth, et al. 2023\.  Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum.  _JAMA internal medicine_ (2023).
  * Bandura (1989) Albert Bandura. 1989\.  Human agency in social cognitive theory.  _American psychologist_ 44, 9 (1989), 1175.
  * Barthakur et al. (2023) Abhinava Barthakur, Shane Dawson, and Vitomir Kovanovic. 2023\.  Advancing leaner profiles with learning analytics: A scoping review of current trends and challenges. In _LAK23: 13th International Learning Analytics and Knowledge Conference_. 606–612.
  * Berg et al. (2016) Alan Mark Berg, Stefan T Mol, Gábor Kismihók, and Niall Sclater. 2016.  The role of a reference synthetic data generator within the field of learning analytics.  _Journal of Learning Analytics_ 3, 1 (2016), 107–128.
  * Buckingham Shum et al. (2019) Simon Buckingham Shum, Rebecca Ferguson, and Roberto Martinez-Maldonado. 2019.  Human-centred learning analytics.  _Journal of Learning Analytics_ 6, 2 (2019), 1–9.
  * Cao et al. (2023) Jie Cao, Ananya Ganesh, Jon Cai, Rosy Southwell, E Margaret Perkoff, Michael Regan, Katharina Kann, James H Martin, Martha Palmer, and Sidney D’Mello. 2023\.  A Comparative Analysis of Automatic Speech Recognition Errors in Small Group Classroom Discourse. In _Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization_. 250–262.
  * Charleer et al. (2017) Sven Charleer, Andrew Vande Moere, Joris Klerkx, Katrien Verbert, and Tinne De Laet. 2017\.  Learning analytics dashboards to support adviser-student dialogue.  _IEEE Transactions on Learning Technologies_ 11, 3 (2017), 389–399.
  * Chiu (2023) Thomas KF Chiu. 2023\.  The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney.  _Interactive Learning Environments_ (2023), 1–17.
  * Choi et al. (2023) Jonathan H Choi, Kristin E Hickman, Amy Monahan, and Daniel Schwarcz. 2023.  Chatgpt goes to law school.  _Available at SSRN_ (2023).
  * Chua et al. (2019) Yi Han Victoria Chua, Justin Dauwels, and Seng Chee Tan. 2019\.  Technologies for automated analysis of co-located, real-life, physical learning spaces: Where are we now?. In _Proceedings of the 9th International Conference on Learning Analytics and Knowledge_. 11–20.
  * Clow (2012) Doug Clow. 2012\.  The learning analytics cycle: closing the loop effectively. In _LAK’12_. 134–138.
  * Corrin et al. (2017) Linda Corrin, Paula G De Barba, and Aneesha Bakharia. 2017\.  Using learning analytics to explore help-seeking learner profiles in MOOCs. In _Proceedings of the seventh international learning analytics & knowledge conference_. 424–428.
  * Cui et al. (2023) Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023.  Chatlaw: Open-source legal large language model with integrated external knowledge bases.  _arXiv preprint arXiv:2306.16092_ (2023).
  * Dai et al. (2023) Wei Dai, Jionghao Lin, Flora Jin, Tongguang Li, Yi-Shan Tsai, Dragan Gasevic, and Guanliang Chen. 2023.  Can large language models provide feedback to students? A case study on ChatGPT. In _Proceedings of the 23rd IEEE International Conference on Advanced Learning Technologies_.
  * D’Mello and Jensen (2022) Sidney D’Mello and Emily Jensen. 2022.  Emotional Learning Analytics.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 120–129.
  * Dorodchi et al. (2019) Mohsen Dorodchi, Erfan Al-Hossami, Aileen Benedict, and Elise Demeter. 2019.  Using synthetic data generators to promote open science in higher education learning analytics. In _2019 IEEE International Conference on Big Data (Big Data)_. IEEE, 4672–4675.
  * Dowell and Kovanović (2022) Nia Dowell and Vitomir Kovanović. 2022.  Modeling Educational Discourse with Natural Language Processing.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 105–119.
  * Dworkin (2014) Gerald Dworkin. 2014\.  ‘Paternalism’.  In _Reading Political Philosophy_. Routledge, 342–356.
  * Echeverria et al. (2018) Vanessa Echeverria, Roberto Martinez-Maldonado, Simon Buckingham Shum, Katherine Chiluiza, Roger Granda, and Cristina Conati. 2018.  Exploratory versus explanatory visual learning analytics: Driving teachers’ attention through educational data storytelling.  _Journal of Learning Analytics_ 5, 3 (2018), 73–97.
  * Fan et al. (2022) Yizhou Fan, Joep van der Graaf, Lyn Lim, Mladen Raković, Shaveen Singh, Jonathan Kilgour, Johanna Moore, Inge Molenaar, Maria Bannert, and Dragan Gašević. 2022.  Towards investigating the validity of measurement of self-regulated learning based on trace data.  _Metacognition and Learning_ 17, 3 (2022), 949–987.
  * Fiorella and Kuhlmann (2020) Logan Fiorella and Shelbi Kuhlmann. 2020.  Creating drawings enhances learning by teaching.  _Journal of Educational Psychology_ 112, 4 (2020), 811\.
  * Flanagan et al. (2022) Brendan Flanagan, Rwitajit Majumdar, and Hiroaki Ogata. 2022\.  Fine grain synthetic educational data: challenges and limitations of collaborative learning analytics.  _IEEE Access_ 10 (2022), 26230–26241.
  * Forsyth et al. (2016) Bryan Forsyth, Carmen Kimble, James Birch, Gary Deel, and Trisha Brauer. 2016.  Maximizing the Adaptive Learning Technology Experience.  _Journal of Higher Education Theory & Practice_ 16, 4 (2016).
  * Gašević et al. (2015) Dragan Gašević, Shane Dawson, and George Siemens. 2015\.  Let’s not forget: Learning analytics are about learning.  _TechTrends_ 59 (2015), 64–71.
  * Gebreegziabher et al. (2023) Simret Araya Gebreegziabher, Zheng Zhang, Xiaohang Tang, Yihao Meng, Elena L Glassman, and Toby Jia-Jun Li. 2023.  Patat: Human-ai collaborative qualitative coding with explainable interactive rule synthesis. In _CHI’2023_. 1–19.
  * Gibson et al. (2017) Andrew Gibson, Adam Aitken, Ágnes Sándor, Simon Buckingham Shum, Cherie Tsingos-Lucas, and Simon Knight. 2017.  Reflective writing analytics for actionable feedback. In _Proceedings of the seventh international learning analytics & knowledge conference_. 153–162.
  * Gibson and Shibani (2022) Andrew Gibson and Antonette Shibani. 2022.  Natural Language Processing - Writing Analytics.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 96–104.
  * Gray and Bergner (2022) Geraldine Gray and Yoav Bergner. 2022.  A Practitioner’s Guide to Measurement in Learning Analytics: Decisions, Opportunities, and Challenges.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 20–28.
  * Gris et al. (2023) Lucas Rafael Stefanel Gris, Ricardo Marcacini, Arnaldo Candido Junior, Edresson Casanova, Anderson Soares, and Sandra Maria Aluísio. 2023\.  Evaluating OpenAI’s Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person.  _arXiv preprint arXiv:2305.14580_ (2023).
  * Gursoy et al. (2016) Mehmet Emre Gursoy, Ali Inan, Mehmet Ercan Nergiz, and Yucel Saygin. 2016.  Privacy-preserving learning analytics: challenges and techniques.  _IEEE Transactions on Learning technologies_ 10, 1 (2016), 68–81.
  * Ham et al. (2020) Donghoon Ham, Jeong-Gwan Lee, Youngsoo Jang, and Kee-Eung Kim. 2020\.  End-to-end neural pipeline for goal-oriented dialogue systems using GPT-2. In _Proceedings of the 58th annual meeting of the association for computational linguistics_. 583–592.
  * Han et al. (2021) Jeongyun Han, Kwan Hoon Kim, Wonjong Rhee, and Young Hoan Cho. 2021.  Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation.  _Computers & Education_ 163 (2021), 104041.
  * Hannafin and Land (1997) Michael J Hannafin and Susan M Land. 1997.  The foundations and assumptions of technology-enhanced student-centered learning environments.  _Instructional science_ 25 (1997), 167–202.
  * Hauptman et al. (2023) Allyson I Hauptman, Beau G Schelble, Nathan J McNeese, and Kapil Chalil Madathil. 2023.  Adapt and overcome: Perceptions of adaptive autonomous agents for human-AI teaming.  _Computers in Human Behavior_ 138 (2023), 107451.
  * Herodotou et al. (2019) Christothea Herodotou, Martin Hlosta, Avinash Boroowa, Bart Rienties, Zdenek Zdrahal, and Chrysoula Mangafa. 2019.  Empowering online teachers through predictive learning analytics.  _British Journal of Educational Technology_ 50, 6 (2019), 3064–3079.
  * Hooshyar et al. (2023) Danial Hooshyar, Kairit Tammets, Tobias Ley, Kati Aus, and Kaire Kollom. 2023.  Learning Analytics in Supporting Student Agency: A Systematic Review.  _Sustainability_ 15, 18 (2023), 13662.
  * Jääskelä et al. (2021) Päivikki Jääskelä, Ville Heilala, Tommi Kärkkäinen, and Päivi Häkkinen. 2021\.  Student agency analytics: Learning analytics as a tool for analysing student agency in higher education.  _Behaviour & Information Technology_ 40, 8 (2021), 790–808.
  * Järvelä et al. (2015) Sanna Järvelä, Paul A Kirschner, Ernesto Panadero, Jonna Malmberg, Chris Phielix, Jos Jaspers, Marika Koivuniemi, and Hanna Järvenoja. 2015.  Enhancing socially shared regulation in collaborative learning groups: Designing for CSCL regulation tools.  _Educational Technology Research and Development_ 63 (2015), 125–142.
  * Järvelä et al. (2023a) Sanna Järvelä, Andy Nguyen, and Allyson Hadwin. 2023a.  Human and artificial intelligence collaboration for socially shared regulation in learning.  _British Journal of Educational Technology_ (2023).
  * Järvelä et al. (2023b) Sanna Järvelä, Andy Nguyen, Eija Vuorenmaa, Jonna Malmberg, and Hanna Järvenoja. 2023b.  Predicting regulatory activities for socially shared regulation to optimize collaborative learning.  _Computers in Human Behavior_ 144 (2023), 107737.
  * Järvelä et al. (2023c) Sanna Järvelä, Guoying Zhao, Janne Heikkilä, Hanna Järvenoja, Kristina Mikkonen, and Satu Kaleva. 2023c.  Hybrid Intelligence–Human-AI Co-Evolution and Learning in Multirealities (HI).  In _HHAI 2023: Augmenting Human Intellect_. IOS Press, 392–394.
  * Jivet et al. (2017) Ioana Jivet, Maren Scheffel, Hendrik Drachsler, and Marcus Specht. 2017.  Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. In _Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings 12_. Springer, 82–96.
  * Jivet et al. (2018) Ioana Jivet, Maren Scheffel, Marcus Specht, and Hendrik Drachsler. 2018.  License to evaluate: Preparing learning analytics dashboards for educational practice. In _Proceedings of the 8th international conference on learning analytics and knowledge_. 31–40.
  * Joksimovic et al. (2023) Srecko Joksimovic, Dirk Ifenthaler, Rebecca Marrone, Maarten De Laat, and George Siemens. 2023\.  Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review.  _Computers and Education: Artificial Intelligence_ (2023), 100138.
  * Karaoglan Yilmaz and Yilmaz (2020) Fatma Gizem Karaoglan Yilmaz and Ramazan Yilmaz. 2020.  Student opinions about personalized recommendation and feedback based on learning analytics.  _Technology, knowledge and learning_ 25 (2020), 753–768.
  * Kasneci et al. (2023) Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al. 2023\.  ChatGPT for good? On opportunities and challenges of large language models for education.  _Learning and individual differences_ 103 (2023), 102274.
  * Koester (2015) Brenda Koester. 2015\.  University of Michigan source code and data associated with practical learning analytics course.  https://www.solaresearch.org/about/what-is-learning-analytics/.  Accessed: 2023-09-24.
  * Kovanović et al. (2018) Vitomir Kovanović, Srećko Joksimović, Negin Mirriahi, Ellen Blaine, Dragan Gašević, George Siemens, and Shane Dawson. 2018.  Understand students’ self-reflections through learning analytics. In _Proceedings of the 8th international conference on learning analytics and knowledge_. 389–398.
  * Kung et al. (2023) Tiffany H Kung, Morgan Cheatham, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, Maria Madriaga, Rimel Aggabao, Giezel Diaz-Candido, James Maningo, et al. 2023\.  Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models.  _PLoS digital health_ 2, 2 (2023), e0000198.
  * Lang et al. ([n. d.]) Charles Lang, Alyssa Friend Wise, Agathe Merceron, Dragan Gašević, and George Siemens. [n. d.].  What is Learning Analytics?  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 8–18.
  * Le et al. (2023) Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, et al. 2023\.  Voicebox: Text-guided multilingual universal speech generation at scale.  _arXiv preprint arXiv:2306.15687_ (2023).
  * Lee et al. (2023) Unggi Lee, Ariel Han, Jeongjin Lee, Eunseo Lee, Jiwon Kim, Hyeoncheol Kim, and Cheolil Lim. 2023.  Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data.  _Education and Information Technologies_ (2023), 1–31.
  * Lewis et al. (2020) Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020\.  Retrieval-augmented generation for knowledge-intensive nlp tasks.  _Advances in Neural Information Processing Systems_ 33 (2020), 9459–9474.
  * Li et al. (2023a) Xinyu Li, Yizhou Fan, Mladen Raković, Shaveen Singh, Joep van der Graaf, Lyn Lim, Johanna Moore, Inge Molenaar, Maria Bannert, and Dragan Gašević. 2023a.  FLoRA: Facilitating Self-Regulated Learning with Personalised Scaffolds on Students’ own Regulation Activities.  _arXiv preprint_ (2023).
  * Li et al. (2023b) Yuheng Li, Lele Sha, Lixiang Yan, Jionghao Lin, Mladen Raković, Kirsten Galbraith, Kayley Lyons, Dragan Gašević, and Guanliang Chen. 2023b.  Can large language models write reflectively.  _Computers and Education: Artificial Intelligence_ 4 (2023), 100140\.
  * Lim et al. (2021) Lisa-Angelique Lim, Sheridan Gentili, Abelardo Pardo, Vitomir Kovanović, Alexander Whitelock-Wainwright, Dragan Gašević, and Shane Dawson. 2021.  What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course.  _Learning and Instruction_ 72 (2021), 101202.
  * Long et al. (2007) Louise Long, Sean MacBlain, and Martin MacBlain. 2007\.  Supporting students with dyslexia at the secondary level: An emotional model of literacy.  _Journal of Adolescent & Adult Literacy_ 51, 2 (2007), 124–134.
  * Martinez-Maldonado et al. (2020) Roberto Martinez-Maldonado, Vanessa Echeverria, Gloria Fernandez Nieto, and Simon Buckingham Shum. 2020\.  From data to insights: A layered storytelling approach for multimodal learning analytics. In _Proceedings of the 2020 chi conference on human factors in computing systems_. 1–15.
  * Matcha et al. (2019) Wannisa Matcha, Dragan Gašević, Abelardo Pardo, et al. 2019\.  A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective.  _IEEE transactions on learning technologies_ 13, 2 (2019), 226–245.
  * Mayer (2005) Richard E Mayer. 2005\.  Cognitive theory of multimedia learning.  _The Cambridge handbook of multimedia learning_ 41 (2005), 31–48.
  * Mazzoli et al. (2023) Carlo Alberto Mazzoli, Federico Semeraro, and Lorenzo Gamberini. 2023.  Enhancing Cardiac Arrest Education: Exploring the potential use of MidJourney.  _Resuscitation_ 189 (2023).
  * McCalla and Champaign (2013) Gord McCalla and John Champaign. 2013.  Simulated learners.  _IEEE Intelligent Systems_ 28, 04 (2013), 67–71.
  * McCarthy (2015) Josh McCarthy. 2015\.  Evaluating written, audio and video feedback in higher education summative assessment tasks.  _Issues in Educational Research_ 25, 2 (2015), 153–169.
  * Mikolov et al. (2013) Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013.  Distributed representations of words and phrases and their compositionality.  _Advances in neural information processing systems_ 26 (2013).
  * Molenaar (2022) Inge Molenaar. 2022\.  The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning.  _Computers and Education: Artificial Intelligence_ 3 (2022), 100070\.
  * Motz et al. (2023) Benjamin A Motz, Yoav Bergner, Christopher A Brooks, Anna Gladden, Geraldine Gray, Charles Lang, Warren Li, Fernando Marmolejo-Ramos, and Joshua D Quick. 2023.  A LAK of direction: Misalignment between the goals of learning analytics and its research scholarship.  _Journal of Learning Analytics_ (2023), 1–13.
  * National Academies of Sciences, Engineering, and Medicine and others (2021) National Academies of Sciences, Engineering, and Medicine and others. 2021.  Human-AI teaming: State-of-the-art and research needs.  (2021).
  * Nieto et al. (2022) Gloria Milena Fernandez Nieto, Kirsty Kitto, Simon Buckingham Shum, and Roberto Martinez-Maldonado. 2022\.  Beyond the learning analytics dashboard: Alternative ways to communicate student data insights combining visualisation, narrative and storytelling. In _International Conference on Learning Analytics and Knowledge 2022_. Association for Computing Machinery (ACM), 219–229.
  * Ochoa (2022) Xavier Ochoa. 2022\.  Multimodal Learning Analytics - Rationale, Process, Examples, and Direction.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 54–65.
  * Ochoa et al. (2018) Xavier Ochoa, Federico Domínguez, Bruno Guamán, Ricardo Maya, Gabriel Falcones, and Jaime Castells. 2018.  The RAP system: Automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors. In _LAK’18_. 360–364.
  * Orlando (2016) John Orlando. 2016\.  A comparison of text, voice, and screencasting feedback to online students.  _American Journal of Distance Education_ 30, 3 (2016), 156–166.
  * Papamitsiou et al. (2020) Zacharoula Papamitsiou, Michail N Giannakos, and Xavier Ochoa. 2020\.  From childhood to maturity: are we there yet? Mapping the intellectual progress in learning analytics during the past decade. In _Proceedings of the tenth international conference on learning analytics & knowledge_. 559–568.
  * Pardo et al. (2018) Abelardo Pardo, Kathryn Bartimote, Simon Buckingham Shum, Shane Dawson, Jing Gao, Dragan Gašević, Steven Leichtweis, Danny Liu, Roberto Martínez-Maldonado, Negin Mirriahi, et al. 2018\.  Ontask: Delivering data-informed, personalized learning support actions.  (2018).
  * Pardo et al. (2019) Abelardo Pardo, Jelena Jovanovic, Shane Dawson, Dragan Gašević, and Negin Mirriahi. 2019\.  Using learning analytics to scale the provision of personalised feedback.  _British Journal of Educational Technology_ 50, 1 (2019), 128–138.
  * Park et al. (2023) Joon Sung Park, Joseph C O’Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023.  Generative agents: Interactive simulacra of human behavior.  _arXiv preprint arXiv:2304.03442_ (2023).
  * Pontual Falcão et al. (2020) Taciana Pontual Falcão, Rafael Ferreira Mello, and Rodrigo Lins Rodrigues. 2020.  Applications of learning analytics in Latin America.  , 871–874 pages.
  * Radford et al. (2021) Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021\.  Learning transferable visual models from natural language supervision. In _International conference on machine learning_. PMLR, 8748–8763.
  * Rahman et al. (2022) Md Mostafizer Rahman, Yutaka Watanobe, Taku Matsumoto, Rage Uday Kiran, and Keita Nakamura. 2022\.  Educational data mining to support programming learning using problem-solving data.  _IEEE Access_ 10 (2022), 26186–26202.
  * Rao (2023) Ashwin Rao. 2023\.  Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos.  _arXiv preprint arXiv:2307.03200_ (2023).
  * Reardon et al. (2022) Emily Reardon, Vishesh Kumar, and Glenda Revelle. 2022\.  Game Learning Analytics.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 152–162.
  * Reeve (2013) Johnmarshall Reeve. 2013\.  How students create motivationally supportive learning environments for themselves: The concept of agentic engagement.  _Journal of educational psychology_ 105, 3 (2013), 579\.
  * Reworkd (2023) Reworkd. 2023.  Introduction to AgentGPT.  https://docs.reworkd.ai/introduction.  Accessed: 2023-09-24.
  * Rietz and Maedche (2021) Tim Rietz and Alexander Maedche. 2021.  Cody: An AI-based system to semi-automate coding for qualitative research. In _CHI’2021_. 1–14.
  * Roberts et al. (2017) Lynne D Roberts, Joel A Howell, and Kristen Seaman. 2017\.  Give me a customizable dashboard: Personalized learning analytics dashboards in higher education.  _Technology, Knowledge and Learning_ 22 (2017), 317–333.
  * Roll and Winne (2015) Ido Roll and Philip H Winne. 2015.  Understanding, evaluating, and supporting self-regulated learning using learning analytics.  _Journal of Learning Analytics_ 2, 1 (2015), 7–12.
  * Rudolph et al. (2023) Jürgen Rudolph, Samson Tan, and Shannon Tan. 2023.  ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?  _Journal of Applied Learning and Teaching_ 6, 1 (2023).
  * Schumacher and Ifenthaler (2018) Clara Schumacher and Dirk Ifenthaler. 2018.  Features students really expect from learning analytics.  _Computers in human behavior_ 78 (2018), 397–407.
  * Selwyn (2019) Neil Selwyn. 2019\.  What’s the problem with learning analytics?  _Journal of Learning Analytics_ 6, 3 (2019), 11–19.
  * Siemens et al. (2022) George Siemens, Fernando Marmolejo-Ramos, Florence Gabriel, Kelsey Medeiros, Rebecca Marrone, Srecko Joksimovic, and Maarten de Laat. 2022\.  Human and artificial cognition.  _Computers and Education: Artificial Intelligence_ 3 (2022), 100107\.
  * Singer et al. (2022) Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. 2022\.  Make-a-video: Text-to-video generation without text-video data.  _arXiv preprint arXiv:2209.14792_ (2022).
  * Slee (2018) Roger Slee. 2018\.  Defining the scope of inclusive education.  (2018).
  * SOLAR (2021) SOLAR. 2021.  What is Learning Analytics?  https://www.solaresearch.org/about/what-is-learning-analytics/.  Accessed: 2023-09-25.
  * Song et al. (2022) Wenfeng Song, Xia Hou, Shuai Li, Chenglizhao Chen, Danyang Gao, Yuzhe Sun, Jianxia Hou, Aimin Hao, et al. 2022\.  An Intelligent Virtual Standard Patient for Medical Students Training Based on Oral Knowledge Graph.  _IEEE Transactions on Multimedia_ (2022).
  * Suero Montero and Suhonen (2014) Calkin Suero Montero and Jarkko Suhonen. 2014.  Emotion analysis meets learning analytics: online learner profiling beyond numerical data. In _Proceedings of the 14th Koli calling international conference on computing education research_. 165–169.
  * Terwiesch (2023) Christian Terwiesch. 2023\.  Would chat GPT3 get a Wharton MBA.  _A prediction based on its performance in the operations management course. Wharton: Mack Institute for Innovation Management/University of Pennsylvania/School Wharton_ (2023).
  * Tsai and Gasevic (2017) Yi-Shan Tsai and Dragan Gasevic. 2017.  Learning analytics in higher education—challenges and policies: a review of eight learning analytics policies. In _Proceedings of the seventh international learning analytics & knowledge conference_. 233–242.
  * Tsai and Martinez-Maldonado (2022) Yi-Shan Tsai and Roberto Martinez-Maldonado. 2022.  Human-Centered Approaches to Data-Informed Feedback.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 213–222.
  * Tsai et al. (2020) Yi-Shan Tsai, Alexander Whitelock-Wainwright, and Dragan Gašević. 2020\.  The privacy paradox and its implications for learning analytics. In _Proceedings of the tenth international conference on learning analytics & knowledge_. 230–239.
  * Turner and Patrick (2008) Julianne C Turner and Helen Patrick. 2008.  How does motivation develop and why does it change? Reframing motivation research.  _Educational psychologist_ 43, 3 (2008), 119–131.
  * Van Dis et al. (2023) Eva AM Van Dis, Johan Bollen, Willem Zuidema, Robert van Rooij, and Claudi L Bockting. 2023\.  ChatGPT: five priorities for research.  _Nature_ 614, 7947 (2023), 224–226.
  * Vartiainen and Tedre (2023) Henriikka Vartiainen and Matti Tedre. 2023.  Using artificial intelligence in craft education: crafting with text-to-image generative models.  _Digital Creativity_ 34, 1 (2023), 1–21.
  * Vaswani et al. (2017) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017.  Attention is all you need.  _Advances in neural information processing systems_ 30 (2017).
  * Verbert et al. (2020) Katrien Verbert, Xavier Ochoa, Robin De Croon, Raphael A Dourado, and Tinne De Laet. 2020\.  Learning analytics dashboards: the past, the present and the future. In _Proceedings of the tenth international conference on learning analytics & knowledge_. 35–40.
  * Viberg et al. (2018) Olga Viberg, Mathias Hatakka, Olof Bälter, and Anna Mavroudi. 2018.  The current landscape of learning analytics in higher education.  _Computers in human behavior_ 89 (2018), 98–110.
  * Wang et al. (2023a) Lei Wang, Xijin Ge, Li Liu, and Gangqing Hu. 2023a.  Code Interpreter for Bioinformatics: Are We There Yet?  _Annals of Biomedical Engineering_ (2023), 1–3.
  * Wang et al. (2023b) Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, et al. 2023b.  Rodin: A generative model for sculpting 3d digital avatars using diffusion. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_. 4563–4573.
  * Westerlund (2019) Mika Westerlund. 2019\.  The emergence of deepfake technology: A review.  _Technology innovation management review_ 9, 11 (2019).
  * Winne (2022) Philip H. Winne. 2022\.  Learning Analytics for Self-Regulated Learning.  In _The Handbook of Learning Analytics_ (2 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, and Agathe Merceron (Eds.). SoLAR, 78–85.
  * Winne and Hadwin (1998) P. H. Winne and A. F. Hadwin. 1998.  Studying as self-regulated learning.  In _Metacognition in educational theory and practice_ , D. J. Hacker, J. Dunlosky, and A. C. Graesser (Eds.). Lawrence Erlbaum Associates Publishers, 277–304.
  * Wise et al. (2021) Alyssa F Wise, Simon Knight, and Xavier Ochoa. 2021\.  What makes learning analytics research matter.  _Journal of Learning Analytics_ 8, 3 (2021), 1–9.
  * Xiao et al. (2023) Ziang Xiao, Xingdi Yuan, Q Vera Liao, Rania Abdelghani, and Pierre-Yves Oudeyer. 2023.  Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding. In _Companion Proceedings of the 28th International Conference on Intelligent User Interfaces_. 75–78.
  * Xing et al. (2020) Wanli Xing, Hee-Sun Lee, and Antonette Shibani. 2020.  Identifying patterns in students’ scientific argumentation: content analysis through text mining using Latent Dirichlet Allocation.  _Educational Technology Research and Development_ 68 (2020), 2185–2214.
  * Yan et al. (2023) Lixiang Yan, Lele Sha, Linxuan Zhao, Yuheng Li, Roberto Martinez-Maldonado, Guanliang Chen, Xinyu Li, Yueqiao Jin, and Dragan Gašević. 2023.  Practical and ethical challenges of large language models in education: A systematic scoping review.  _British Journal of Educational Technology_ 00 (2023), 1–23.
  * Yan et al. (2022) Lixiang Yan, Linxuan Zhao, Dragan Gasevic, and Roberto Martinez-Maldonado. 2022.  Scalability, sustainability, and ethicality of multimodal learning analytics. In _LAK22: 12th international learning analytics and knowledge conference_. 13–23.
  * Yildirim-Erbasli and Bulut (2023) Seyma N Yildirim-Erbasli and Okan Bulut. 2023.  Conversation-based assessment: A novel approach to boosting test-taking effort in digital formative assessment.  _Computers and Education: Artificial Intelligence_ 4 (2023), 100135\.
  * Zambrano et al. (2023) Andres Felipe Zambrano, Xiner Liu, Amanda Barany, Ryan Shaun Baker, Juhan Kim, and Nidhi Nasiar. 2023.  From nCoder to ChatGPT: From Automated Coding to Refining Human Coding.  (2023).  https://doi.org/10.35542/osf.io/grmzh
  * Zamecnik et al. (2022) Andrew Zamecnik, Vitomir Kovanović, Srećko Joksimović, and Lin Liu. 2022.  Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study.  _Computers and Education: Artificial Intelligence_ 3 (2022), 100051\.
  * Zhang et al. (2021) Rui Zhang, Nathan J McNeese, Guo Freeman, and Geoff Musick. 2021.  ” An ideal human” expectations of AI teammates in human-AI teaming.  _Proceedings of the ACM on Human-Computer Interaction_ 4, CSCW3 (2021), 1–25.
  * Zhang and Linn (2011) Zhihui Helen Zhang and Marcia C Linn. 2011.  Can generating representations enhance learning with dynamic visualizations?  _Journal of research in science teaching_ 48, 10 (2011), 1177–1198.
  * Zhao et al. (2023) Linxuan Zhao, Zachari Swiecki, Dragan Gašević, Lixiang Yan, Samantha Dix, Hollie Jaggard, Rosie Wotherspoon, Abra Osborne, Xinyu Li, Riordan Alfredo, and Roberto Martinez-Maldonado. 2023\.  METS: Multimodal Learning Analytics of Embodied Teamwork Learning. In _Proceedings of the 13th International Conference on Learning Analytics and Knowledge_ (Arlington, TX, USA) _(LAK2023)_. 186–196.
  * Zhou and Winne (2012) Mingming Zhou and Philip H Winne. 2012.  Modeling academic achievement by self-reported versus traced goal orientation.  _Learning and Instruction_ 22, 6 (2012), 413–419.
  * Zimmerman (2000) Barry J Zimmerman. 2000\.  Attaining self-regulation: A social cognitive perspective.  In _Handbook of self-regulation_. Elsevier, 13–39.