LAOps: Learning Analytics with Privacy-aware MLOps
Pia Niemel
, Bilhanan Silverajan
, Mikko Nurminen
, Jenni Hukkanen
and Hannu-Matti J
Faculty of Information Technology and Communication Sciences, Tampere University,
P.O. Box 1001 FI-33014, Tampere, Finland
Learning Management System, Next-generation Learning Environment, Assessment and Feedback, Learning
Analytics, Personalisation, Machine Learning, Privacy-aware Machine Learning, Cloud-based Learning
Analysis, MLOps, LAOps.
The intake of computer science faculty has rapidly increased with simultaneous reductions to course personnel.
Presently, the economy is recovering slightly, and students are entering the working life already during their
studies. These reasons have fortified demands for flexibility to keep the target graduation time the same as
before, even shorten it. Required flexibility is created by increasing distance learning and MOOCs, which
challenges students’ self-regulation skills. Teaching methods and systems need to evolve to support students’
progress. At the curriculum design level, such learning analytics tools have already been taken into use. This
position paper outlines a next-generation, course-scope analytics tool that utilises data from both the learning
management system and Gitlab, which works here as a channel of student submissions. Gitlab provides
GitOps, and GitOps will be enhanced with machine learning, thereby transforming as MLOps. MLOps that
performs learning analytics, is called here LAOps. For analysis, data is copied to the cloud, and for that, it
must be properly protected, after which models are trained and analyses performed. The results are provided
to both teachers and students and utilised for personalisation and differentiation of exercises based on students’
skill level.
Learning management systems (LMSs) have grown
in prominence in course management, practising and
performance evaluation. In Tampere University, for
example, the LMS system auto-tests and grades stu-
dents’ submissions, including the bigger coursework
assignments. The grading data in software courses
may comprise, e.g., unit/integration test reports and
static code analysis. In total, the pipeline of auto-tests
generates an excessive amount of data together with
the data gathered from code commits and the project
management tool. In essence, Tampere University
already utilises student data and learning analytics
for tutoring and smoothing the transfer from higher
education to working life (Okkonen et al., 2020b;
a and Okkonen, 2021; Okkonen et al., 2020a),
as a broader-scope target than the one of this research.
Instead of using proprietary scripts for manipu-
lating and analyzing e-Learning data, the pipeline
should be smartened up with Data/MLOps to make
the analysis automatic, traceable, observable and se-
cure. This way analysis remains well-documented
and results are readily available for all project partic-
ipants, and for them only by a guarantee. During the
analysis phase, the data will be transferred into the
cloud. The solution architecture targets bridging the
learning analytics development and the identified best
practices of secure data storage and handling.
LMS platforms rely heavily on cloud-based stor-
age for user data pertaining to each user’s learning
patterns, personal data, results obtained and interac-
tions. The cybersecurity aspect of this paper is to
maximise and fortify the trust of users in cloud-based
LMS services by developing mechanisms for protect-
ing both derived and raw personal data. The paper
then offers a novel solution through which both users
and machine learning algorithms can calculate statis-
tics or operate on data in a privacy-preserving way.
This paper is organised as follows. Chapter 2
Niemelä, P., Silverajan, B., Nurminen, M., Hukkanen, J. and Järvinen, H.
LAOps: Learning Analytics with Privacy-aware MLOps.
DOI: 10.5220/0011113300003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 2, pages 213-220
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
presents machine learning operations (MLOps) for
education and learning analytic systems, started by
the introduction of MLOps approaches for analytics.
The next chapter merges MLOps and learning analyt-
ics and reviews how learning analytics can be used
to enhance the LMSs. After that, we present a few
approaches to privacy-aware machine learning, nec-
essary to keep students’ data safe and secure. Finally,
we illustrate the data flow from the current LMSs to
LAOps, which is the term coined to describe learning-
analytics-targeted MLOps. Chapter 6 summarises the
anticipated benefits and disadvantages for both stu-
dents and teachers.
In the digitizing world, enormous amounts of data are
collected into various databases. Machine learning
(ML) utilises data to provide predictions and recom-
mendations. Especially, the steps taken in the field
of deep neural networks to improve prediction accu-
racy combined with improved computational capaci-
ties have enabled its use in complex data sets and in-
creased interest in ML. This increased interest in ap-
plying ML to analytics has created demands for eas-
ily accessible ML pipelines. In addition, there have
been increasing amounts of requests for more sensi-
ble reuse of ML components and data cleaning code
(Fursin, 2020).
The steps made in the field of DevOps to ease
and automatise software development has motivated
to implement similar approaches to the field of ML.
MLOps is an approach to make the ML pipeline col-
laborative, reproducible, reusable, and trustable.
Figure 1 displays an overview of the MLOps
pipeline, which is simplified from one of the most fa-
mous versions of MLOps, Continuous Delivery for
Machine Learning (CD4ML) (Sato et al., 2019), and
the other MLOps version (Granlund et al., 2021). It
can be seen from Figure 1 that the MLOps pipeline
can be divided into three parts: 1) the data science
part, where the models are built, experimented and
evaluated; 2) the actual ML model part; and 3) the
production part, where the model is deployed, taken
into production, and monitored.
In the data science part of the MLOps pipeline,
data engineering is often a considerable task due to
various data formats and computer systems. The qual-
ity of the curated learning data is paramount for accu-
rate prediction (Renggli et al., 2021; M
akinen et al.,
2021; Valohai ltd., 2020). Data collection must be
planned right from the start, preferably with data sci-
entists, to ensure the quality of the data and that the
data is easily accessible from relevant databases. Data
quality metrics can be used to indicate how qual-
ity criteria correlate with the learning process, and
how this should reflect in the MLOps pipeline de-
sign (Renggli et al., 2021).
Monitoring is an important part of the MLOps to
ensure the performance of the system and to restart
the model building if the performance starts to devi-
ate. The cycle to restart the model building and up-
date the model depends on the application. Develop-
ing predictive ML models may be laborious, the ac-
cess to the training data may be limited, or there are
reasons that the model is wanted to be frozen, such as
in some critical medical applications (Granlund et al.,
2021). Thus, the motivation to start building a new
ML model is higher than it would be in some other
MLOps is especially suited for environments,
where ML is used either in multiple organisations or
in hybrid-cloud environments (Granlund et al., 2021;
Banerjee et al., 2020) since the idea of the MLOps
is that all the parts of the system should efficiently
communicate with each other, there is version con-
trol so that the results are reproducible, and the tools
should be reusable. An increasing number of com-
panies provide services at least to some part of the
MLOps pipeline. For instance, Google Cloud Au-
toML advertises itself to enable ”high-quality custom
machine learning models with minimal effort and ma-
chine learning expertise.
The explainability and sustainability of models is
on the rise, and they are also mandated by the EU
regulations in the case of critical applications (Tam-
burri, 2020). A well-known challenge of especially
deep neural networks based ML algorithms is that the
explainability of the output is limited so that the al-
gorithms are used more or less as a black box (Adadi
and Berrada, 2018). There are research efforts to in-
crease the explainability of the deep neural networks,
such as heatmap analysis developed for the image
data sets (Borg et al., 2021). These kinds of analy-
sis blocks should be installed in the MLOps pipeline
to test the bias of the model and to increase the trans-
parency of the system. The other challenge of the ML
systems is that the training data may be biased by gen-
der, ethnically or in some other way causing ethical
issues (Safdar et al., 2020). The bias can be alleviated
e.g. by slicing the training data set in different ways.
In the proposed system, the ML is meant to give
recommendations and to improve students’ and teach-
ing personnel’s situational awareness. The course
CSEDU 2022 - 14th International Conference on Computer Supported Education
Figure 1: An overview of the MLOps pipeline.
grading is done separately from the ML; hence, the
possibly biased training data does not affect the grad-
ing. However, the bias should be kept in mind when
developing the ML system so that the system does not
amplify the existing structures, e.g. the better students
would get better advice. To circumvent the challenge,
students could see all the possible advice, while the
system recommends the advice that is expected to be
the most helpful. The students mark their preference,
thereby teaching the system for more accurate advis-
ing in the future.
As group sizes increase, it is no longer possible for
course personnel to know their students or establish
any more meaningful relationship with them. In this
situation, the importance of a pedagogically function-
ing LMS and good communication practices becomes
central. The pedagogy-savvy LMS may take the form
of an interactive eTextbook, provide so-called ”smart
content” and include features of intelligent tutoring
with personalized exercises. Caricatured, this kind
of a tutor takes responsibilities that used to belong to
In Finland, examples of more interactive LMSs
are the following: TIM (short for The Interactive Ma-
terial), VILLE and Plussa. TIM is developed by the
University of Jyv
a and it realizes an eTextbook-
type of approach, which besides interactive content
enables, e.g., students to leave notes to the side of
lecture slides, making it easy for the instructor to
pick out the objects to improve (Isom
onen et al.,
2019; Tirronen et al., 2020). Of the mentioned LMSs,
VILLE excels in learning analytics (Rajala et al.,
2007; Laakso et al., 2018). VILLE group has system-
atically developed the environment since 2007, spear-
headed by analysis and research purposes. Currently
in VILLE, formative assessment can be used as an
early indicator of dropping out and as a prompt for
course personnel to start necessary countermeasures
(Veerasamy et al., 2021)
Plussa enables such smart content as program-
ming content examples, programming instructions
(Hosseini et al., 2020; Brusilovsky et al., 2018),
and animations of algorithms (Sirki
a, 2018; Hos-
seini et al., 2020). In Plussa, exercises can even be
fully-fledged DevOps-simulating assignments where
student groups follow the agile methodology, and
graders execute unit and end-to-end tests after each
submission (Nurminen et al., 2021). One of the
best features of Plussa is its microservice-based ar-
chitecture which makes it easily extendable, ex-
emplified by the easy introduction of new graders
a and Hyyr
o, 2019). Another is the support
for Learning Tools Interoperability (LTI) protocol
(IMS Global Learning Consortium and others, 2019)
that would enable plugging-in exercises in external
servers (Manzoor et al., 2019), even if they resided
in different continents (Brusilovsky et al., 2018).
Machine learning adds value to the LMS by col-
lecting statistics, by automatic elaborations of the
course content (both learning material and material
produced by students), and by tracing and modelling
the behaviour of students, which in turn enables dif-
ferentiation, personalising and customisation of exer-
cises. Automatic elaboration would mean, e.g., ex-
tractions of concepts and keywords, and automatic an-
notations. To be able to identify the key concepts is
crucial in comprehending topics to learn. As a sub-
ject, computer science is similar to mathematics that
is built on learning trajectories where conceptual un-
derstanding gradually develops and deepens, while
LAOps: Learning Analytics with Privacy-aware MLOps
the concepts gradually become more complex. The
concepts must be internalized which implies linking
the concept to adjacent and parallel concepts in the
schema. If the concept is crucial for progress, i.e.,
only partial or no comprehension will deteriorate or
prevent the progress, the concept is called a threshold
concept (Boustedt et al., 2007). Automatic detection
of such concepts would be helpful.
Tracing the student behaviour may also lead to
material rearrangements, if it is noticed that the learn-
ing trace is unnecessarily complicated by students
having to bounce from one place to another in the ma-
terial. The learning process information can also be
processed as recommendations, such as suggestions
for extra reading, links to helpful exercises, in partic-
ular such exercises that would better help to solve the
troublesome task at hand. For example, if it is found
that a similarly profiled student appears to be partic-
ularly benefiting from a particular representation or
receiving a eureka moment after some programming
content examples, the very same material may be of-
fered to another student in the corresponding profile
category. Irrespective of ML usage, the recommen-
dation and hints can also be queried directly from
students as one type of exercise and offered to other
students of the same profile in the same cluster, in
a crowd-sourced manner. Within clusters, students
share some kind of submissions, activity, and perfor-
mance level. The clusters can also be utilized in group
formation, to achieve more reasoned divisions.
LMS can be built to support the weak and to
engage ill-motivated students. To avoid frustra-
tion/boredom, the challenge level must be in accor-
dance with the skill level of a student. Preferably,
LMS dynamically differentiates the challenge level
by having collected information on students’ perfor-
mance. In automatized follow-ups, LMS can also uti-
lize students’ self-reflections and such commitments
as a target grade in obligating changes to activity
level, if needed. Reflective discussions could be han-
dled by a chatbot, e.g., in situations where results de-
teriorate rapidly. With an analogue of a sports watch,
LMS can serve students with statistics, and increase
their awareness of one’s own performance in relation
both to previous personal performance and of the one
of the whole group. Comparably to sports watches,
formative assessment on-a-go is anticipated to im-
prove self-efficacy and self-regulation, needed in dis-
tance learning and MOOCs, where learning is more
autonomous by definition.
Learning analytic mandates modelling of a stu-
dent. The modelling has developed considerably in
recent years and there are several different alterna-
tives available, e.g., student modelling (Chau et al.,
2021), open student modelling (Bull and Kay, 2013),
even open social student modelling (Brusilovsky and
Rus, 2019). Modelling is such a big effort, so it
would be desirable for the models to be interchange-
able between different courses, even different sys-
tems. Reuse would require smarter mapping, e.g.,
with the help of ontologies (Chau et al., 2021).
Naturally, in the beginning, the setup of an ML
system requires extra time and effort. After driven-
in, the disadvantages of ML usage are increased con-
trol and decrease of privacy, at least if privacy issues
are dealt poorly with the LMS. The potential opacity
of the system would stir up fears of misuse of data.
Preferably, a teacher could consult an expert, address
the ethical concerns in particular, and ensure trans-
parency as far as possible. Transparency is increased
also by informing students about on-going analyses
and research aims. It is good to explain what is the ra-
tionale behind automatically made conclusions, i.e.,
to respond to the demand for explainability of back-
ground algorithms. In compliance, a few articles em-
phasized the need for more controllable and explain-
able recommendations to add to the transparency of
the system (Chacon and Sosnovsky, 2021). Also, if
data must be transferred elsewhere to enable heavier
computations, it must be handled with all the needed
care and safety.
In many cases, LMSs store user data about both learn-
ers and educators. Such data can include personal
details, as well as interactions, navigation patterns,
learning patterns, results and grades obtained, com-
munication with other actors as well as possible rea-
sons where anomalous behaviour (such as long pe-
riods of absence, or unusual and different quality
of submissions) is noticed. While many LMSs are
running on each organisation’s own infrastructure, in
many instances, such data is stored or sent to the
cloud, where machine-learning algorithms and appli-
cations may be employed to derive subsequent meta-
data. Such a cloud-based service may be offered
by an external or commercial service provider. A
major challenge here is the protection and privacy
of such sensitive user data when considering cloud-
based compute and storage services. Sensitive and
private data may unintentionally or deliberately be
made available to unauthorised third parties or insid-
ers, including situations where these LMSs may even
run on local infrastructure. Nevertheless, the overar-
ching issue remains unchanged: Preventing access to
CSEDU 2022 - 14th International Conference on Computer Supported Education
data stored in any locations to any user without the
correct credentials. In addition, the different means
of protecting data needs to mirror the wide variety of
stakeholders and organisations.
Today, data is usually sent to a cloud service
provider (CSP) using a RESTful API over a secure
channel. This typically implies transport layer secu-
rity, in which a server is validated by a certificate. By
default, a user sends data without encryption. How-
ever, the CSP must ensure that data is stored and
maintained in an encrypted form. Most existing ap-
proaches do not manage to protect users’ data against
attacks of either adept external adversaries or insid-
ers. Two properties of the CSP can be attributed to
this inadequacy: a bulk data storage and encryption
function. Vulnerabilities in these CSP’s properties at-
tract attackers. An attacker could even become privy
to the data, having mounted a successful attack. Re-
search in the field of Symmetric Searchable Encryp-
tion (SSE) provides promising solutions to overcome
such challenges.
SSE schemes allow users to encrypt data with
a symmetric key unknown to the CSP and search
directly over the encrypted data. However, SSE
schemes discourage a user from sharing data with
other users, as sharing a symmetrically-encrypted file
requires sharing the secret key. As a result, when a
user needs to be revoked, the data owner needs to re-
encrypt the data with a fresh key and distribute the
new key to the remaining legitimate users. To address
the problem of revocation, researchers proposed so-
lutions based on Attribute-Based Encryption schemes
(ABE) (Michalas, 2019).
ABE schemes allow users to encrypt a file based
on a certain policy. Then, a unique key is generated
for each user that has access to the CSP resources.
This key is generated based on a list of attributes. A
user can decrypt a file encrypted with a certain policy
if and only if the attributes of her key satisfy the un-
derlying policy. In this case, access revocation is more
efficient: it only requires revoking the corresponding
user’s key, while the keys of all other users remain the
same. However, the access control flexibility enabled
by ABE schemes comes at a cost: the generated ABE
cipher-texts are rather large. As a result, decryption
requires significant computational resources (Micha-
las, 2019).
Another challenge in LMSs is to run statistical
and analytical ML functions on large datasets with-
out revealing the identities of individuals related to
the data. This challenge can be addressed by the de-
sign and implementation of a set of protocols based
on another promising cryptographic primitive called
Functional Encryption (FE). Functional encryption is
a new paradigm in public-key encryption that allows
users to finely control the amount of information that
is revealed by a ciphertext to a given receiver (Ab-
dalla et al., 2015). Thus, statistical computations can
be performed based on encrypted data allowing sen-
sitive data to remain protected during computation.
Analysts will be unable to obtain revealing data as a
To protect the privacy of users and prevent sev-
eral types of malicious behaviour (which includes in-
sider threats), several types of modern cryptography
approaches are needed to construct a privacy-aware
cloud-based LMS. Privacy protection can be strength-
ened in different ways, e.g.: by enabling SSE and
ABE to use both encryption schemes in the most effi-
cient way in order to store and process user data, or by
utilizing effective user access revocation approaches
that do not affect other users or the overall functional-
ity of the service. These enable education profession-
als to generate analytics and statistical measurements
in a privacy-preserving way.
Currently deployed teaching systems at Tampere Uni-
versity include LMSs like Moodle, Plussa, and Gitlab;
others exist yet lesser in relevance. During program-
ming courses, students commit their code to reposi-
tories provided to them using Gitlab. The data from
LMSs and Gitlab is the main focus of the proposed
LAOps solution. Figure 2 displays an example of how
data from current LMS systems’ databases could flow
to secure LAOps processes in the cloud. In the im-
age, we can see two possible approaches for fetching
data to LAOps, which can be applied simultaneously.
In the first approach, LAOps processes periodically
fetch data through the APIs provided by the LMS
components. In the latter approach, LMS components
that have Continuous Integration or Continuous De-
livery pipelines integrate sending data to LAOps pro-
cesses into these pipelines.
The steps listed below detail the process for the
fore-mentioned two approaches:
1. Instructor constructs upstream repositories: stu-
dent template project and group template project
with the help of a self-made Gitlab management
tool; these upstream repositories are updated reg-
ularly by the instructor.
2. Student pulls upstream, gets instructions, a) com-
mits his/her code either to student repository if
submitting alone, or b) to group repository in case
of group work.
LAOps: Learning Analytics with Privacy-aware MLOps
Figure 2: The interplay of Plussa, GitLab and secure LAOps in the proposed scenario.
3. After content with the code, a student initiates the
grading in Plussa learning management system by
submitting their git URL there.
4. Plussa clones and grades the student’s submission,
and the generated grades and reports are stored in
Plussa’s database.
5. A student’s commit to Gitlab launches a Gitlab
CI pipeline, which has been set up to send the
anonymised data to LAOps.
6. Plussa provides a RESTful API (no CI/CD
pipeline), LAOps components will periodically
fetch anonymised data through its API.
7. LAOps performs MLOps as presented in Figure 1:
LAOps saves the incoming data to the database in
a privacy-secure way, gives predictions and rec-
ommendations based on the developed ML algo-
rithms, and monitors the performance of the algo-
rithms to initialize the updates to the algorithms.
8. The necessary scripts for handling and visualising
the dataset that LAOps accumulates are available
in upstream repositories. Students may, if inter-
ested, check the statistics and see the dashboard
of the progress of the course.
The anonymisation of the student data is crucial,
as it enables using the data in analytics performed
in the cloud resources located outside the control of
Tampere University. However, most LMSs have APIs
which give students data as-is, uncensored. This ne-
cessitates that anonymisation is implemented now as
part of this proposal.
During the risk analysis stage other data secu-
rity matters pertaining to using sensitive personal data
with LAOps were identified. These included the pri-
vacy matters as discussed in the previous chapter, as
well as other general data security concerns including:
which access control mechanisms and processes
need to be put in place in the cloud as well in our
research group so that access to the data and sec-
tions of data can be limited on per user basis
how to find and apply CSP-dependant best prac-
tices on building cloud systems in a way that
reduces or even eliminates the chance of data
how we can demonstrate that the process we use
for anonymisation of the personal data is on suf-
ficient level, and able to cope with changes in the
learning systems from where data is fetched.
We have proposed an architecture for learning ana-
lytic system, LAOps, which is secure and adaptable
for both students and teachers. The architecture is
motivated by the recent developments of MLOps as
well as privacy-aware cryptographic data storage in
the cloud. The aimed benefits of LAOps for the stu-
better-informed intelligent tutoring, scaffolding
personalisation / customisation / differentiation
recommendations (reading recommendations,
useful assignments), analogies, metaphors
statistics, awareness of one’s own performance in
relation to the whole group
CSEDU 2022 - 14th International Conference on Computer Supported Education
increased self-direction and autonomy
better-justified creation of groups based on profil-
Benefits for the teacher:
better statistics / better understanding of the cur-
rent status
profiling of students, e.g., for early detection of
dropouts, or teamwork problems
detection of useful vs. challenging tasks, more
precise identification of threshold concepts
hints for material improvement, rearrangement
and replenishment
automatic annotations, keyword search.
The usage of cryptographic schemes for data stor-
age as well as for privacy-preserving analytics, is es-
sential to alleviate fears of leakage of private data to
untrusted third parties. Additionally increased user
involvement and awareness that the LMS has been
designed with security in mind, aid in reducing per-
ceptions that invasive machine learning methods are
employed on personally identifiable information. The
regulation of LA is under development, new stricter
and refined legislation and rules are to be expected.
Abdalla, M., Bourse, F., Caro, A. D., and Pointcheval, D.
(2015). Simple functional encryption schemes for
inner products. In IACR International Workshop on
Public Key Cryptography, pages 733–751. Springer.
Adadi, A. and Berrada, M. (2018). Peeking inside the black-
box: A survey on explainable artificial intelligence
(XAI). IEEE Access, 6:52138–52160.
Banerjee, A., Chen, C., Hung, C., Huang, X., Wang, Y., and
Chevesaran, R. (2020). Challenges and experiences
with MLOps for performance diagnostics in hybrid-
cloud enterprise software deployments. In Talagala,
N. and Young, J., editors, 2020 USENIX Conference
on Operational Machine Learning, OpML 2020, July
28 - August 7, 2020, pages 37–39. USENIX Associa-
Borg, M., Jabangwe, R.,
Aberg, S., Ekblom, A., Hed-
lund, L., and Lidfeldt, A. (2021). Test automation
with grad-CAM Heatmaps - A future pipe segment in
MLOps for Vision AI? In 14th IEEE International
Conference on Software Testing, Verification and Val-
idation Workshops, ICST Workshops 2021, Porto de
Galinhas, Brazil, April 12-16, 2021, pages 175–181.
Boustedt, J., Eckerdal, A., McCartney, R., Mostr
om, J. E.,
Ratcliffe, M., Sanders, K., and Zander, C. (2007).
Threshold concepts in computer science: do they exist
and are they useful? ACM Sigcse Bulletin, 39(1):504–
Brusilovsky, P., Malmi, L., Hosseini, R., Guerra, J., Sirki
T., and Pollari-Malmi, K. (2018). An integrated prac-
tice system for learning programming in Python: de-
sign and evaluation. Research and Practice in Tech-
nology Enhanced Learning, 13(1):1–40.
Brusilovsky, P. and Rus, V. (2019). Social navigation for
self-improving intelligent educational systems. De-
sign Recommendations for Intelligent Tutoring Sys-
tems, page 131.
Bull, S. and Kay, J. (2013). Open learner models as drivers
for metacognitive processes. In International Hand-
book of Metacognition and Learning Technologies,
pages 349–365. Springer.
Chacon, I. A. and Sosnovsky, S. A. (2021). Knowledge
models from PDF textbooks. New Review of Hyper-
media and Multimedia, 27(1-2):128–176.
Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky,
P. (2021). Automatic concept extraction for domain
and student modeling in adaptive textbooks. Interna-
tional Journal of Artificial Intelligence in Education,
Fursin, G. (2020). The Collective Knowledge project:
making ML models more portable and reproducible
with open APIs, reusable best practices and MLOps.
CoRR, abs/2006.07161.
Granlund, T., Kopponen, A., Stirbu, V., Myllyaho, L.,
and Mikkonen, T. (2021). Mlops challenges in
multi-organization setup: Experiences from two real-
world cases. In 2021 IEEE/ACM 1st Workshop on
AI Engineering-Software Engineering for AI (WAIN),
pages 82–88. IEEE.
a, H. and Okkonen, J. (2021). AI driven competency
development at the threshold of working life. In 2021
Nordic Learning Analytics (Summer) Institute, NLASI
2021. CEUR-WS.
Hosseini, R., Akhuseyinoglu, K., Brusilovsky, P., Malmi,
L., Pollari-Malmi, K., Schunn, C., and Sirki
a, T.
(2020). Improving engagement in program construc-
tion examples for learning Python programming. In-
ternational Journal of Artificial Intelligence in Educa-
tion, 30(2):299–336.
IMS Global Learning Consortium and others (2019). Learn-
ing tools interoperability core specification. IMS Final
Release Version, 1.
onen, V., Lakanen, A.-J., and Lappalainen, V.
(2019). Less is more! Preliminary evaluation of multi-
functional document-based online learning environ-
ment. In 2019 IEEE Frontiers in Education Confer-
ence (FIE), pages 1–5. IEEE.
Laakso, M.-J., Kurvinen, E., Enges-Pyyk
onen, P., and
Kaila, E. (2018). Designing and creating a framework
for learning analytics in Finland. In 2018 41st Inter-
national Convention on Information and Communi-
cation Technology, Electronics and Microelectronics
(MIPRO), pages 0695–0700. IEEE.
akinen, S., Skogstr
om, H., Laaksonen, E., and Mikko-
nen, T. (2021). Who needs MLOps: What data sci-
entists seek to accomplish and how can MLOps help?
In 2021 IEEE/ACM 1st Workshop on AI Engineering-
Software Engineering for AI (WAIN), pages 109–112.
LAOps: Learning Analytics with Privacy-aware MLOps
Manzoor, H., Akhuseyinoglu, K., Shaffer, C., and
Brusilovsky, P. (2019). OpenDSA/Mastery Grids Ex-
ercise Interchange. In Proceedings of the Fourth
SPLICE Workshop colocated with 50th ACM Tech-
nical Symposium on Computer Science Education
(SIGSCE 2019), Minneapolis, MN, USA.
Michalas, A. (2019). The lord of the shares: Combin-
ing attribute-based encryption and searchable encryp-
tion for flexible data sharing. In Proceedings of the
34th ACM/SIGAPP Symposium on Applied Comput-
ing, pages 146–155.
a, P. and Hyyr
o, H. (2019). Migrating learning man-
agement systems towards microservice architecture.
Joint Proceedings of the Inforte Summer School on
Software Maintenance and Evolution (SSSME-2019),
page 11.
Nurminen, M., Niemel
a, P., and J
arvinen, H.-M. (2021).
Having it all: auto-graders reduce workload yet in-
crease the quantity and quality of feedback. In SEFI
Annual Conference: Blended Learning in Engineer-
ing Education: challenging, enlightening–and last-
ing, pages 385–393.
Okkonen, J., Helle, T., and Lindsten, H. (2020a). Ethical
considerations on using learning analytics in Finnish
higher education. In International Conference on Ap-
plied Human Factors and Ergonomics, pages 77–85.
Okkonen, J., Helle, T., and Lindsten, H. (2020b). Expec-
tation differences between students and staff of using
learning analytics in Finnish universities. In Interna-
tional Conference on Information Technology & Sys-
tems, pages 383–393. Springer.
Rajala, T., Laakso, M.-J., Kaila, E., and Salakoski, T.
(2007). VILLE: a language-independent program vi-
sualization tool. In Proceedings of the Seventh Baltic
Sea Conference on Computing Education Research-
Volume 88, pages 151–159.
Renggli, C., Rimanic, L., Merve G
urel, N., Karla
s, B., Wu,
W., and Zhang, C. (2021). A data quality-driven view
of MLOps. arXiv e-prints, pages arXiv–2102.
Safdar, N. M., Banja, J. D., and Meltzer, C. C. (2020). Eth-
ical considerations in artificial intelligence. European
Journal of Radiology, 122:108768.
Sato, D., Wilder, A., and Windheuser, C. (2019).
Continuous delivery for machine learning. ac-
cessed Feb 15, 2022.
a, T. (2018). JSVEE & Kelmu: Creating and tailoring
program animations for computing education. Journal
of Software: Evolution and Process, 30(2):e1924.
Tamburri, D. A. (2020). Sustainable MLOps: Trends and
challenges. In 22nd International Symposium on Sym-
bolic and Numeric Algorithms for Scientific Comput-
ing, SYNASC 2020, Timisoara, Romania, September
1-4, 2020, pages 17–23. IEEE.
Tirronen, V., Lappalainen, V., Isom
onen, V., Lakanen,
A.-J., Taipalus, T., Nieminen, P., and Ogbechie, A.
(2020). Incorporating teacher-student dialogue into
digital course material: Usage patterns and first ex-
periences. In 2020 IEEE Frontiers in Education Con-
ference (FIE), pages 1–5. IEEE.
Valohai ltd. (2020). Practical MLOps.
Veerasamy, A. K., Laakso, M.-J., and D’Souza, D. (2021).
Formative assessment tasks as indicators of student
engagement for predicting at-risk students in pro-
gramming courses. Informatics in Education.
CSEDU 2022 - 14th International Conference on Computer Supported Education