教育機関における学習分析の設計と導入のフレームワーク：文献レビュー
林 晶晶 1), 原田 耕治 1), 後藤 仁志 1),2)
1) 豊橋技術科学大学
2) 豊橋技術科学大学

IT 活用教育センター

情報メディア基盤センター

lin@cite.tut.ac.jp

Frameworks of Designing and Implementing Learning Analytics in
Educational Institutions: A Review
Jingjing Lin 1), Kouji Harada 1), Hitoshi Goto 1),2)
1) Center for IT-based Education, Toyohashi University of Technology
2) Information and Media Center, Toyohashi University of Technology

Abstract
Learning analytics represents a transformative approach to understanding and enhancing the
educational landscape. As higher education institutions recognize and embrace the potential oflearning analytics, they unlock myriad benefits, fostering a more responsive and effective
educational ecosystem. However, the design and implementation of learning analytics in theorganization is a complicated process of planning, communicating, collaborating, anddecision-making that involves multiple stakeholders, both inside and outside the organization. The
paper examines 15 learning analytics models that discuss how to design and implement learning
analytics in institutions. Four types of frameworks are identified: domain, process, level, and mixed.
A holistic planner of organizational learning analytics is created because of the review.

1 Introduction
Learning analytics is defined as the measurement,collection, analysis and reporting of data about learners
and their contexts, for purposes of understanding andoptimizing learning and the environments in which it
occurs [1]. Learning analytics aims to customize
educational opportunities based on the specific needs
and abilities of each learner, achieved through actions
like intervening with at-risk students and offering
feedback and instructional content [2]. The
development and progress of this endeavor are
intricate, interdependent, and constantly evolving,
involving a diverse range of stakeholders throughout
the institution [3]. However, limited research isavailable on perspectives and involvement of all
stakeholder groups in institutional planning andimplementation of learning analytics, and on the ways
how institutions develop and enact strategies for thedesigning and implementation of learning analytics[4].
In 2022, the Center for IT-based Education (CITE)
at Toyohashi University of Technology (TUT or

369

Gikadai), a national university in Japan, decided to
explore the possibility of integrating learning analytics
into the university’s Moodle-based learning
management system (LMS). To accomplish this,
discussions were held with several learning analytics
solution providers. After a thorough evaluation andcareful comparison, IntelliBoard, a vendor specializingin learning analytics for LMS platforms such as
Moodle, was selected for a pilot initiative called TUT
Learning Analytics (TUT–LA). The initiative aims at
designing, deploying, and implementing learning
analytics with the involvement of key stakeholders at
TUT. Considering the practical intent and the existing
research gap, it is our contention that undertaking a
research investigation to document this university-led
endeavor would yield significant advantages for bothacademic institutions and educational researchers witha shared interest in the domain of learning analytics
within the higher education context.
Furthermore,in
conjunction
withtheGikadai–MDA (Mathematics, Data science, and AI
education, or in Japanese as GIKADAI 数理・データ
サ イ エ ン ス ・ AI 教 育 プ ロ グ ラ ム ) programimplemented at TUT, the TUT–LA initiative is ofparticular importance in the Japanese university
context. After the release of the Cabinet Office’s AI
Strategy policy in 2019 and the establishment of the
MDASH (Mathematics, Data Science, and AI Smart
Higher Education) Programs Certification System in
2021, an expectation has been set that graduates fromJapanese universities, junior colleges, and technicalcolleges should possess a foundational understandingof mathematics, data science, and AI. As shown in Fig.
1, in response to this new policy, between 2021 and
2023, a total of 529 dedicated educational programs
offered by higher education institutions in Japan
received accreditation at the MDASH Literacy Level (リ テ ラ シ ー レ ベ ル : 382) and at the MDASH
Advanced Literacy Level ( 応用基礎レベル: 147).
Remarkably, some programs were specificallyrecognized at the MDASH Literacy+ Level (リテラシー レ ベ ル プ ラ ス : 25) and MDASH Advanced
Literacy+ Level (応用基礎レベルプラス: 14) due totheir innovative and distinctive ideas andcharacteristics [5].

Fig. 1 MDASH programs by 2023 in Japan
In the Gikadai–MDA program, there are fivecourses at the MDASH Literacy+ Level including
Introduction to Information and Communication
Technology, Introduction to Engineering, Engineeringand Science Laboratory, Probability and Statistics,
and Research Project. They are for first- andsecond-year undergraduate students. There are twocourses at the MDASH Advanced Literacy Level
including Data Science Exercise, and Advanced Data
Science Exercise. They are for third- and fourth-year
undergraduate students. The latter two courses use
interactive textbooks in Jupyter Notebook format, theTK Basic series and TK Advanced series, which werecreatedincollaboration
with
KIKAGAKU
Corporation—a provider of business training [6].
Applying learning analytics in the MDASH
programs can unveil a captivating possibility. Over
years, despite the growing body of learning analytics
research, its tangible impact on actual teaching andlearning practices remains somewhat limited, primarily

370

due to the prevailing prevalence of small-scale
adoptions [3], [4], [7]. The research findings derived
from this endeavor possess considerable potential toyield scalable experience and comparable research
projects among all MDASH programs operating
throughout Japan, which actively engage a wide rangeof stakeholders within the comprehensive landscape ofJapanese higher education. This paper reports theexperience from the TUT–LA project by sharing
results of a selective review of the literature that
proposed learning analytics models, which primarily
visualize the learning analytics designing andimplementation at the institutional level. The purposeof the review is to synthesize the accumulated
intelligence on this topic and to offer a holistic andintegrated view to inform the institutional designingand implementation actions of learning analytics at
TUT.

2 Deploying learning analytics
educational institutions: A review
available frameworks

inof

Numerous frameworks that aim at facilitating thedesigning and implementation of learning analytics
within educational institutions are available. Twodedicated review studies exist on providing a holistic
view of learning analytics models [8], [9]. This review
is different from them because its focus is onreviewing learning analytics models that involve thedesigning and implementation of learning analytics at
the institutional level. Our review resulted in a
categorization of these frameworks into four distinct
types: “domain” frameworks, “process” frameworks,“level” frameworks, and “mixed” frameworks. The
domain type of frameworks is more dominant in theliterature than the other three types (Table 1). The
mixed type of frameworks typically mixes process anddomain or level and domain. Hence, be aware that thecount of the mixed type overlaps with the counts of theother three types in Table 1. An overview of these fourtypes of frameworks is presented in Table 2. The
subsequent paragraphs provide comprehensive details
regarding each type.
Table 1. Number of frameworks by type
Type
Number of frameworks
Domain11
Process
4
Level
3
Mixed
2

2.1 “Domain” type of frameworks
Domain frameworks offer comprehensive coverageof various components, aspects, or dimensions thatnecessitate consideration when designing learning
analytics at the institutional level. This type is the mostpopular type among the reviewed frameworks. Out ofeight domain frameworks, four common domains aresynthesized and listed in Table 3, which are objectives,
stakeholders,technology
infrastructure,andinstitutional context.
Objectives. It is recommended that institutions set amaturity index to gauge learning analytics progression,
set goals, and measure progress [10]. There aredifferent objectives, which can be related to for
instance awareness and reflection [11]–[14],behavioral change of students and educators during thelearning processes [11], retention [3], supporting
educational decisions [12], monitoring and analyzing[12], prediction and intervention [12], [13], tutoringand mentoring [12], assessment and feedback,adaptation [12], personalization and recommendation[12], identifying at-risk students/courses/subjects [14],informing educational practices [14], helping/advising
students [14], developing new programs [14], holding
academics accountable for student performance [14],scholarly
research[14],
andinfluencing
faculty/university policy [14]. Defining objectives,
which reflect back to the “purpose” of adopting
learning analytics in the first place [15], will influence
forthcoming choices and actions in other domains ofconsideration.
Stakeholders. This domain, on one hand define,
who to involve in the learning analytics process, which
may include both internal stakeholders (e.g., students,
teaching staff, senior managers, researchers, study
program leaders, IT officers, a working group made up
of representatives from various units) and external
stakeholders (e.g., learning analytics experts, data
scientists, service/solution vendors, publishers, data
governing bodies) [13], [15], [16]. Another suggestion
is to divide stakeholders into data clients (beneficiariesof the learning analytics process to act upon theoutcome, e.g., teachers) and data subjects (suppliers ofdata, through their browsing and interaction behaviors,e.g., students), and to expand stakeholder groups toalso include computer agents (e.g., notification email
sent by system) [13]. On the other hand, the domain of
stakeholders is also related to assessing stakeholders’perceptions, attitudes, willingness [17], motives, needs[3], skills [14], [17], providing them with necessary
training to get start [3], [14], as well as monitoring andsupporting them in practice [14]. For instance, it was371

suggested that learning analytics researchers should
study and share experiences on the effects ofintegrating learning analytics into everyday practice[12]. The current lack of support and communication
academics receive when faced with learning analytics
should be improved [18].
Technology infrastructure. It comprises the “basic
enterprise technology environment for individual
institutions, including the combination of data,
information, reporting, and analytics capabilities” [17,p. 31]. This domain covers consideration aspects suchas what data will be collected [10], [12], [15], [19],what systems and environments will generate thesedata [12], [15], what analytics tools, techniques, andmethods will be used to analyze the collected data[10], [12], [13], [19], what technical solutions/services
are available (what can/cannot be technically achieved)[15], what is the data policy to guarantee a satisfying
level of ethics, privacy, and data stewardship [3], [11],[13], [16], and how sustainable are the learning
analytics solutions. The various objectives need the
support of tailored set of performance indicators andmetrics so that they can become easier to measure,monitor, and evaluate [12]. This domain has several
challenges to address. For instance, the scope of data
(e.g., collect data that did not accurately reflect student
activities) [18], the quality of data (e.g., data accuracy,
data reliability, data completion) [3], [14], [18],stakeholders’ understanding and trust of the benefits ofusing learning analytics are required to ensure thesustainability of these tools [20], choosing the rightformat to present the data and insight effectively to
stakeholders [3], and system’s interface being notuser-friendly enough [3].
Institutional context. This domain covers “hard”
tangible context such as human resources [3], [15],financial resources [10], and administrative processes[17], and “soft” intangible context such as governance[10], [21], policies [14], [17], strategies [3], [14],leadership or top management support [14], [16], [22],culture/conventions/norms [3], [10], [14], [21]. It’s
worth noting that some elements, like governance,
policies, and strategy, can be seen as straddlingbetween soft and hard contexts. For instance, while
strategy might emerge from intangible organizational
values and perspectives, it often gets codified into
tangible plans and actions. Institutions arerecommended to perform an audit of various
contextual components such as policy, processes, andpractices to see what supports student success andwhat has become an impediment [17].

1
2

3
4

5

6
7

Table 2. Frameworks of deploying learning analytics at the institutional level
Framework
Year Type
Components
Four-level analytics
2007 Leveldescriptive analytics; predictive analytics;
sophistication [23]prescriptive analytics; autonomous analytics
The five steps of2007 Processcapturing; reporting; predicting; acting; refining
analytics [16]Domain stakeholders; information technology; goals andexpectations; organizational readiness
Learning analytics
2012 Processdata collection and pre-processing; analysis andprocess [12]action; post-processing
Four-component
2012 Domain why; what; who; howreference model forlearning analytics[12]
Six-dimension
2012 Domain stakeholders; objective; data; instruments;
generic design
external limitations; internal limitations
framework forlearning analytics[13]
EDUCAUSE learning 2012 Domain culture/process; data/reporting/tools; investment;
analytics model [10]expertise; governance/infrastructure

Instrument
–
–

–
–

–

ECAR
analytics
survey 2012–

Learning analytics
cycle [24]Learning Analytics
Sophistication Model[7]
Stages of student
success analytics [17]2012

Processlearners; data; metrics; interventions

2013

Level

–

2013

Mixed:
Level
Mixed:
DomainLearning Analytics
Readiness Instrument(LARI) [21]Let’s Talk Learning
Analytics Framework
[3]

2014

Domain2016

Domainawareness; experimentation; adoption;organizational transformation; sectortransformation
static reporting (2-3 years); dynamic analysis andintervention (3-5 years); optimization.
technology infrastructure, analytic tools andapplications; policies, processes, practices, andworkflows; values and skills of stakeholders;
culture and behaviors; leadershipgovernance and infrastructure; ability; data;
culture and process; overall readiness ofperception
institutional context; transitional institutional
elements; learning analytics infrastructure;transitional retention elements; learning analytics
for retention; intervention and reflection

12

SHEILA framework
[15]

2018

Domainpurpose; methodology; management; stakeholder
engagement; ethics and privacy; data
management; human resources; internal andexternal support; financial resources;
infrastructure; culture; capabilities; policy
management; evaluation; policy management

SHEILA: a
web tool

13

Quality indicators forlearning analytics[11]
Five critical successfactors for learning
analytics
implementation [14]Learning analytics
framework [19]2019

Domainobjectives; learning support; learning measuresand output; data aspects; organizational aspects

–

2020

DomainSurveyinstrument

2021

Mixed:
Process
Mixed:
Domainstrategy and policy at the organizational level;
information technological readiness;
performance and impact evaluation; people’s
skills and expertise; data quality
design; development; analysis; assessment

8
9

10
11

14

15

identification of the key stakeholders;
identification of the needs; mapping the available
data; metrics and indicators definition; datacollection approach; data analysis methods;
visualization techniques

372

–

LARI
instrument(90 items)
Academiclevel survey–

Table 3. Four domains to consider when deploying learning analytics at the institutional level
DomainDetails[3] [11] [10] [12] [13] [14] [15] [16] [17] [19]Objectives
what goals,
✔
✔
✔
✔
✔
✔
✔
(success/performance)
Stakeholders who, perception and
✔
✔
✔
✔
✔
✔
✔
✔
✔
motives, readiness,abilities, training andsupportTechnology
data (raw data, indicators,
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
infrastructure metrics, data sources:
systems and environments),
instruments (analytics tools,
techniques, methods),
vendors’ provided services
and solutions,ethics/privacy/stewardship,sustainability
Institutional
soft context (e.g.,
✔
✔
✔
✔
✔
✔
✔
✔
context
governance, policies,
strategy, leadership,culture/conventions/norms),
hard context (e.g., humanresources, financial
resources, administrative
processes)
2.2 “Process” type of frameworks
Process frameworks elucidate sequential steps orprocedures that facilitate the effective designing andimplementation of learning analytics. This type has avertical focus on data management, in comparison to
the domain type that casts a horizontal view to reachdifferent domains of consideration. Table 4 comparedfive process frameworks and discovered four common
steps in the process of implementing learning analytics.
They are data collection, data analysis and predicting,interventions, and assessing and refining.
Data collection. It collects educational data fromenvironments and systems [12]. Previous studies
identified 17 areas of data [10] or 8 types of data [16]collected by institutions. However, many institutions
have not matured to use these collected data forprediction or strategic actions [10]. The decision ofwhat data is to be collected often takes place in theinitial design stage of a learning analytics project,which needs to be informed by domains ofconsideration [19].
Data analysis. It needs to consider objectives, datatypes, tools, and analytic techniques [12]. The data
analysis can be executed for the purpose of analysis forunderstanding the current situations in the educational

373

[21]

✔

✔

✔

contexts (e.g., descriptive statistics), or analysis forpredicting the future behaviors or developments (e.g.,predictive modeling) [16], [24].
Acting. Taking actions is the primary aim of thewhole learning analytics process [12]. Actions may
range from information to inventions. [16] For instance,
the analytic insights can be communicated withinvolved stakeholders using adequate visualization
techniques, such as traditional reports or visualized
dashboards to users (reporting) [16], [19]. Other acting
can include triggering an automatic (e.g., email alerts)
or manual intervention (e.g., personal phone calls)[16], [24].
Assessing and refining. This self-improvement step
takes place at the end of one iteration of the learning
analytics process. It aims to improve methods andresults of the learning analytics practice [19]. The teamneeds to reflect on the whole process, evaluate theperformance and effectiveness of current practice, anddecide on what refinements should be implemented in
the practice’s next iteration. The refinements can takeplace in areas such as dataset, attributes,
metrics/indicators, variables of analysis, analytic
methods, processes, statistical models [12], [16].

Table 4. Process of learning analytics

2

Step
Data collection
pre-processing
Data analysis

3
4

ActingAssessing and refining

1

andDetails
data sources; data format

[12]

[16]

[19]

[24]

✔

✔

✔

✔

use analytic tools and statistical techniques to run
analysis for understanding or analysis for prediction
reporting to stakeholders; trigger interventions
reflect on the whole process to assess the existing
practice and make adjustment for future practice

✔

✔

✔

✔

✔
✔

✔
✔

✔
✔

✔

2.3 “Level” type of frameworks
Level frameworks depict the varying complexity
and maturity levels of institutional learning analytics’development. It is expected that an institution willneed a couple of years to progress from an immatureorganization in learning analytics to a mature one.
Only three frameworks discussed this progression.
Davenport and Harris [23] introduced a taxonomy
comprising four levels of analytics that reflect thesophistication of the intelligence they provide.
Although their work is not directly addressing learning
analytics, due to its high citations in the learning
analytics research community and repeatedappearances in EDUCAUSE publications such as [10],[17], it is included in this review. The lowest level,
descriptive analytics, can be achieved by standard
reports, ad hoc reports, query/drill down capabilities,
and alerts. Moving up the hierarchy, predictive
analytics
involves
statisticalanalysis,forecasting/extrapolation, and predictive modeling.
Prescriptive analytics encompasses experimental
design and optimization. Autonomous analytics can beattained through the utilization of machine learningtechniques to extract insights from the data, enabling a
deeper understanding of patterns and trends.
Siemens et al. [7] proposed the five-level Learning
Analytics Sophistication Model, which includes:
awareness, experimentation, adoption, organizationaltransformation, and sector transformation. However,this model lacks explanation and elaboration, whichhinders its application in practices.
After surveying 40 higher education institutions in
the USA, the three-level Stages of Student SuccessAnalytics was proposed to “characterize the current
status of organizational capacity for analytics in higher
education as three stages of development” [17, p. 40].
They include Leve 1: static reporting, focuses on data
and reporting; Level 2: dynamic analysis andintervention, focuses on supporting evidence-based
decision making, and Level 3: optimization, focuses onmaking learning analytics a strategic imperative for the
institution. The jump from Level 1 to Level 2 can take
two to three years, and that from Level 2 to Level 3374

can take three to five years. It is worth noting that thedifferentiation of these three development levelborrowed insights of five-factor organizational
capacities of analytics, namely leadership, technology
infrastructure, processes and practices, skills andvalues, and culture and behaviors [17, p. 31]. These
five factors are greatly overlapping with the fourdomains of consideration presented in Table 3, whichsuggests that when designing learning analytics at theinstitutional level considering the organizational
capacity is one primary effort that cannot be
overlooked. Comparing the three “level” type offrameworks, the most relevant and actionable
framework will be this one.
2.4 “Mixed” type of frameworks
Two mixed frameworks [17], [19] blends level anddomain, and process and domain, respectively. The
first mixed framework [17] displays the development
status of five factors of organizational capacity (or fivedomains) in each of the three levels (Level 1, 2, and 3).
The second mixed framework [19] proposes afour-component continuous cycle process of learning
analytics (design, development, analysis, andassessment), and suggests seven stages in the designcomponent (or seven domains). Domains often serve
as repositories for gathering factors to examine anddiscuss when designing and implementing the learning
analytics process, as well as assessing anorganization’s level of learning analytics capacity. Sofar, no mixed framework blends three types (domain,process, level) into one entity.

3 A holistic planner to design andimplement
organizational
learning
analytics
Considering the context of Japan, this study has
created a holistic planner that helps to empower
evidence-based
practiceofdesigningandimplementing organizational learning analytics
through the PDCA cycle (Fig. 1). The PDCA cycle,
developed for quality control modeled after the Toyota
Method, has also been applied to the development ofactionable learning analytics [25]. This plannerintegrates intelligence innated in four types offrameworks and adds to the literature a new mixed
framework that blends three types of frameworks
instead of two types. The planner can serve as an
instructive guide and a stakeholder engagement tool ina learning analytics project. In this planner, the fourdomains are considered in the “plan” stage of the
PDCA cycle, the four-stage process is planted into the“do”, “check”, and “act” stages. This PDCA cycle isrepeated in order to level up the organizational
capacity of learning analytics from the low Level 1 tothe advanced Level 3 over years.

Fig. 1. A holistic planner of designing and implementing organizational learning analytics

4 Final remarks
To utilize this holistic planner, it is crucial for an
educational institution to assemble a multidisciplinary
team. Such a team should include not only educators
and administrators but also data scientists, IT
specialists, and even student representatives. This
diversity allows for a richer understanding of the
intricacies involved in deploying learning analytics,
ensuring that both technical and pedagogical aspects
are adequately addressed. For example, while data
scientists can focus on analytics algorithms and data
integrity, educators can provide insights into how theanalytics can be most useful in a classroom setting.
Administrators can oversee the implementation
process, ensuring it aligns with institutional objectives
and policies.
The necessity of establishing a team is not limited
to the organizational level of designing andimplementing learning analytics but also to thenational level of a similar effort. We propose theestablishment of a national steering group for guiding
the designing of learning analytics in MDASH
programs. This group would bring together experts
from academia, government, and technology sectors tocollaboratively work on the standardization, adoption,
designing, and implementation of learning analytics in

375

MDASH programs nationwide. By acting as a unifying
body, the group could facilitate the sharing of bestpractices, offer recommendations for policy andtechnology integration, and ensure that learning
analytics are employed ethically and effectively acrossvarious educational institutions. The creation of such agroup would signify a national commitment toharnessing the full potential of learning analytics forthe betterment of educational outcomes regarding
mathematics, data science, and AI education in Japan.

5 Conclusion
This paper reviewed and synthesized fifteen
learning analytics frameworks at the organizational
level. It discovered four types of frameworks in theliterature: domain, process, level, and mixed. It createda holistic planner tool to inform organizational practiceof designing and implementing learning analytics. The
purpose of this paper did not entail conducting an
exhaustive examination of all frameworks pertaining tolearning analytics. Instead, it raises the awareness ofpractitioners and researchers on the clustering of theseframeworks under the four types. It is recommended
that future researchers undertake additional
investigations to supplement the existing body ofknowledge within the four identified types.

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