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uoc.edu
uoc.edu
Personalisation of
Education by AI
and Big Data
Prof. Lourdes Guàrdia
Deputy Dean of Psychology and Education at UOC
Leading the Future of Learning
EADTU-EU SUMMIT 2024
Brussels, 08 May
2
Opportunities
● Advances in AI and the availability
of big data are enabling a new era
of personalized education. By
leveraging LA, teachers can gain
deeper insights into student
needs and customize instruction
to support individual and group
learning.
● Data Privacy
● Teacher readiness
● Equity and access
● Scalability and sustainability
● Bias
● Transparency
● Accountability
Personalisation of Education by AI & Big Data
Challenges &
considerations
AI & LA roles and benefits for personalization
Feature Artificial Intelligence (AI) Learning Analytics (LA)
Focus ● Adapting learning experiences based on student data
● Providing data insights to inform personalization
and individualization
Role in
Personalization
● Creates dynamic learning paths with adjusted difficulty,
content type, and resources.
● Recommends relevant learning materials based on
interests and progress.
● Tracks student performance data (quiz scores,
activity completion…).
Role in
Individualization
● Delivers targeted instruction and feedback through
intelligent tutors.
● Uses predictive analytics to identify potential learning
difficulties. Provides automated assessment with
personalized feedback.
● Gathers amounts of data on student behavior and
progress.
● Identifies areas where students might be
struggling.
Benefits for
Teachers
● Frees up time for individualized instruction and
mentorship. Provides data for informed decision-making
on curriculum adjustments.
● Offers insights on student needs and learning
styles, performances…
Benefits for
Students
● Creates a more engaging learning experience.
● Ensures students are challenged at the appropriate level.
● Provides targeted support for individual needs.
● Helps students understand their strengths and
weaknesses.
● Check how the learning progress is going on.
Limitations
● Relies on the quality and completeness of data.
● May not effectively address social-emotional learning
aspects.
● Data alone doesn't dictate personalized
instruction. Requires teacher interpretation for
actionable insights.
Leveraging LA+AI to Support Teachers
AI-driven assessments can
identify individual or group
learning needs and gaps,
enabling targeted
interventions and
personalized learning
pathways.
Personalized
Assessments
Competency Mapping Adaptive Learning
By mapping student
competencies, educators
can better understand their
strengths, weaknesses, and
areas for growth, supporting
lifelong learning.
Adaptive learning
technologies can
dynamically adjust the
content, pace, and difficulty
level based on a student's
performance and progress.
* Images designed by the support of Copilot
Leveraging LA+AI to Support Teachers
LA provides teachers with
real-time data on student
performance, engagement,
and progress, empowering
them to make informed,
evidence-based decisions.
Data-Driven Insights
Personalized
Interventions
Continuous
Improvement
Using LA, teachers can
identify struggling students
and implement targeted
strategies to address their
learning needs.
LA enables teachers to
continuously evaluate and
refine their instructional
approaches, ensuring that
they are meeting the
evolving needs of their
students.
* Images designed by the support of Copilot
Teachers continuously evaluate and refine their instructional approaches
Selecting
suitable
active
pedagogies
Teachers check if activities are supporting learning objectives enough
Checking
e-tivities
design
principles
Follow up report-online Classroom (Canvas-PowerBI example)
Follow up report-online Classroom (Canvas-PowerBI)
Leveraging LA+AI to Support Students
AI and LA can enable
frequent, formative
assessments that provide
real-time feedback to
students and teachers.
Continuous
Assessment
Personalized
Feedback
Automated Grading
Intelligent systems can
generate tailored feedback
and recommendations to
help students understand
their progress and areas for
improvement.
AI-powered grading can provide
consistent, objective evaluations
of student work during the
learning process, specially for
self-assessment, but final
evaluation and validation should
be done by teachers
* Images designed by the support of Copilot
Leveraging LA+AI to Support Students
AI and learning analytics can
enable daily frequent data
and comparative student
progress.
Dashboards for
students
ePortfolio+LA+AI Lifelong Learning
AI-driven ePortfolios can
provide personalized
recommendations and
suggestions to help students
identify their strengths,
address weaknesses, and
set meaningful goals.
ePortfolios and AI can
support students in
developing the essential
skills and competencies
needed for continuous,
lifelong learning.
* Images designed by the support of Copilot
Example: Dashboard for the student
Guàrdia, L., Maina, M., Mancini, F., & Martinez Melo, M. (2023). Key Quality Factors in Digital Competence Assessment: A Validation Study from Teachers’ Perspective.
Applied Sciences, 13(4), 2450. https://doi.org/10.3390/app13042450
Example: Student Competency Map
Example: Personal ePortfolio
uoc.edu
UOC.universitat
@UOCuniversitat
@uocuniversitat
THANKS
Lourdes Guàrdia
lguardia@uoc.edu
@louguardia

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Personalisation of Education by AI and Big Data - Lourdes Guàrdia

  • 1. uoc.edu uoc.edu Personalisation of Education by AI and Big Data Prof. Lourdes Guàrdia Deputy Dean of Psychology and Education at UOC Leading the Future of Learning EADTU-EU SUMMIT 2024 Brussels, 08 May
  • 2. 2 Opportunities ● Advances in AI and the availability of big data are enabling a new era of personalized education. By leveraging LA, teachers can gain deeper insights into student needs and customize instruction to support individual and group learning. ● Data Privacy ● Teacher readiness ● Equity and access ● Scalability and sustainability ● Bias ● Transparency ● Accountability Personalisation of Education by AI & Big Data Challenges & considerations
  • 3. AI & LA roles and benefits for personalization Feature Artificial Intelligence (AI) Learning Analytics (LA) Focus ● Adapting learning experiences based on student data ● Providing data insights to inform personalization and individualization Role in Personalization ● Creates dynamic learning paths with adjusted difficulty, content type, and resources. ● Recommends relevant learning materials based on interests and progress. ● Tracks student performance data (quiz scores, activity completion…). Role in Individualization ● Delivers targeted instruction and feedback through intelligent tutors. ● Uses predictive analytics to identify potential learning difficulties. Provides automated assessment with personalized feedback. ● Gathers amounts of data on student behavior and progress. ● Identifies areas where students might be struggling. Benefits for Teachers ● Frees up time for individualized instruction and mentorship. Provides data for informed decision-making on curriculum adjustments. ● Offers insights on student needs and learning styles, performances… Benefits for Students ● Creates a more engaging learning experience. ● Ensures students are challenged at the appropriate level. ● Provides targeted support for individual needs. ● Helps students understand their strengths and weaknesses. ● Check how the learning progress is going on. Limitations ● Relies on the quality and completeness of data. ● May not effectively address social-emotional learning aspects. ● Data alone doesn't dictate personalized instruction. Requires teacher interpretation for actionable insights.
  • 4. Leveraging LA+AI to Support Teachers AI-driven assessments can identify individual or group learning needs and gaps, enabling targeted interventions and personalized learning pathways. Personalized Assessments Competency Mapping Adaptive Learning By mapping student competencies, educators can better understand their strengths, weaknesses, and areas for growth, supporting lifelong learning. Adaptive learning technologies can dynamically adjust the content, pace, and difficulty level based on a student's performance and progress. * Images designed by the support of Copilot
  • 5. Leveraging LA+AI to Support Teachers LA provides teachers with real-time data on student performance, engagement, and progress, empowering them to make informed, evidence-based decisions. Data-Driven Insights Personalized Interventions Continuous Improvement Using LA, teachers can identify struggling students and implement targeted strategies to address their learning needs. LA enables teachers to continuously evaluate and refine their instructional approaches, ensuring that they are meeting the evolving needs of their students. * Images designed by the support of Copilot
  • 6. Teachers continuously evaluate and refine their instructional approaches Selecting suitable active pedagogies
  • 7. Teachers check if activities are supporting learning objectives enough Checking e-tivities design principles
  • 8. Follow up report-online Classroom (Canvas-PowerBI example)
  • 9. Follow up report-online Classroom (Canvas-PowerBI)
  • 10. Leveraging LA+AI to Support Students AI and LA can enable frequent, formative assessments that provide real-time feedback to students and teachers. Continuous Assessment Personalized Feedback Automated Grading Intelligent systems can generate tailored feedback and recommendations to help students understand their progress and areas for improvement. AI-powered grading can provide consistent, objective evaluations of student work during the learning process, specially for self-assessment, but final evaluation and validation should be done by teachers * Images designed by the support of Copilot
  • 11. Leveraging LA+AI to Support Students AI and learning analytics can enable daily frequent data and comparative student progress. Dashboards for students ePortfolio+LA+AI Lifelong Learning AI-driven ePortfolios can provide personalized recommendations and suggestions to help students identify their strengths, address weaknesses, and set meaningful goals. ePortfolios and AI can support students in developing the essential skills and competencies needed for continuous, lifelong learning. * Images designed by the support of Copilot
  • 12. Example: Dashboard for the student Guàrdia, L., Maina, M., Mancini, F., & Martinez Melo, M. (2023). Key Quality Factors in Digital Competence Assessment: A Validation Study from Teachers’ Perspective. Applied Sciences, 13(4), 2450. https://doi.org/10.3390/app13042450