The document introduces adaptive learning, which adapts educational material based on a student's responses. It discusses modeling student knowledge through knowledge components (KCs), which describe skills, facts, or concepts. Statistical and machine learning models can infer students' knowledge of KCs based on their performance over time. Common models discussed include Bayesian Knowledge Tracing, Item Response Theory, Additive Factor Model, and Performance Factors Analysis. These models calculate the probability students know a KC or will answer correctly based on their abilities and the KC or item difficulties. The goal is to accurately assess student knowledge to provide adaptive feedback and learning experiences.
Regardless of where you are from or how many times you have visited campus, it is important to prepare for your orientation program. The IEC's New Student Orientation Program is the first step in an amazing journey.
Machine learning is concerned with developing algorithms that learn
from experience, build models of the environment from the acquired
knowledge, and use these models for prediction. Machine learning is
usually taught as a bunch of methods that can solve a bunch of
problems (see my Introduction to SML last week). The following
tutorial takes a step back and asks about the foundations of machine
learning, in particular the (philosophical) problem of inductive inference,
(Bayesian) statistics, and arti¯cial intelligence. The tutorial concentrates
on principled, uni¯ed, and exact methods.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Regardless of where you are from or how many times you have visited campus, it is important to prepare for your orientation program. The IEC's New Student Orientation Program is the first step in an amazing journey.
Machine learning is concerned with developing algorithms that learn
from experience, build models of the environment from the acquired
knowledge, and use these models for prediction. Machine learning is
usually taught as a bunch of methods that can solve a bunch of
problems (see my Introduction to SML last week). The following
tutorial takes a step back and asks about the foundations of machine
learning, in particular the (philosophical) problem of inductive inference,
(Bayesian) statistics, and arti¯cial intelligence. The tutorial concentrates
on principled, uni¯ed, and exact methods.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Natural Language Processing for biomedical text mining - Thierry HamonGrammarly
Speaker: Thierry Hamon, Associate Professor in Computer Science at Université Paris, Member of the LIMSI-CNRS research lab.
Summary: Among the large amounts of unstructured data generated across the world and available nowadays, textual data represent an important source of information. This fact is particularly true in the biomedical domain, where a constant increasing demand to access the textual content is observed: the situation is relevant for accessing and processing Electronic Health Records, online discussion forums, and scientific literature. Indeed, dealing with biomedical texts requires us to take into account a great variety of texts, languages and Users.
For several years now, a lot of NLP research has focused on mining and retrieving information (i.e., medical entities and domain-specific relations), which are relevant for biologists, physicians, terminologists, epidemiologists, and patients. We will propose an overview of the NLP methods used for tackling several such research problems through text mining applications. First, we will present the resources and rule-based approaches we designed for extracting drug-related information from clinical texts, and for acquiring domain-specific semantic relations from digital libraries. Then we will present the cross-lingual approach we are developing for building multilingual terminologies from a patient-centered Ukrainian corpus.
Fine-tuning BERT for Question AnsweringApache MXNet
This deck covers the problem of fine-tuning a pre-trained BERT model for the task of Question Answering. Check out the GluonNLP model zoo here for models and tutorials: http://gluon-nlp.mxnet.io/model_zoo/bert/index.html
Slides: Thomas Delteil
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
해당 자료는 풀잎스쿨 18기 중 "설명가능한 인공지능 기획!" 진행 중 Counterfactual Explanation 세션에 대해서 정리한 자료입니다.
논문, Youtube 및 하기 자료를 바탕으로 정리되었습니다.
https://christophm.github.io/interpretable-ml-book/
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
Methods of Optimization in Machine LearningKnoldus Inc.
In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
Natural Language Processing for biomedical text mining - Thierry HamonGrammarly
Speaker: Thierry Hamon, Associate Professor in Computer Science at Université Paris, Member of the LIMSI-CNRS research lab.
Summary: Among the large amounts of unstructured data generated across the world and available nowadays, textual data represent an important source of information. This fact is particularly true in the biomedical domain, where a constant increasing demand to access the textual content is observed: the situation is relevant for accessing and processing Electronic Health Records, online discussion forums, and scientific literature. Indeed, dealing with biomedical texts requires us to take into account a great variety of texts, languages and Users.
For several years now, a lot of NLP research has focused on mining and retrieving information (i.e., medical entities and domain-specific relations), which are relevant for biologists, physicians, terminologists, epidemiologists, and patients. We will propose an overview of the NLP methods used for tackling several such research problems through text mining applications. First, we will present the resources and rule-based approaches we designed for extracting drug-related information from clinical texts, and for acquiring domain-specific semantic relations from digital libraries. Then we will present the cross-lingual approach we are developing for building multilingual terminologies from a patient-centered Ukrainian corpus.
Fine-tuning BERT for Question AnsweringApache MXNet
This deck covers the problem of fine-tuning a pre-trained BERT model for the task of Question Answering. Check out the GluonNLP model zoo here for models and tutorials: http://gluon-nlp.mxnet.io/model_zoo/bert/index.html
Slides: Thomas Delteil
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
해당 자료는 풀잎스쿨 18기 중 "설명가능한 인공지능 기획!" 진행 중 Counterfactual Explanation 세션에 대해서 정리한 자료입니다.
논문, Youtube 및 하기 자료를 바탕으로 정리되었습니다.
https://christophm.github.io/interpretable-ml-book/
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
Methods of Optimization in Machine LearningKnoldus Inc.
In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
A Study on Learning Factor Analysis – An Educational Data Mining Technique fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Ten years ago there were no educational products available for K-12 Math that were truly adaptive. Now just about everyone claims to be adaptive in some way. But what does it mean to be “adaptive”? How do these products work? And how do you evaluate which best fits your needs?
In this presentation, Nigel Green, Vice President of User Experience at DreamBox Learning, discusses the evolving definition of adaptive learning and it's application in varying technologies and approaches, including: how different student actions and behaviors can inform an adaptive engine, how adaptive learning programs can be integrated into your blended learning models, and some of the possible futures of adaptive learning.
Investigating learning strategies in a dispositional learning analytics conte...Bart Rienties
This study aims to contribute to recent developments in empirical studies on students’ learning strategies, whereby the use of trace data is combined with self-report data to distinguish profiles of learning strategy use [3, 4, 5]. We do so in the context of an application of dispositional learning analytics in a large introductory course mathematics and statistics, based on blended learning. Building on our previous work which showed marked differences in how students used worked examples as a learning strategy [7, 11], this study compares different profiles of learning strategies with learning approaches, learning outcomes, and learning dispositions. One of our key findings is that deep learners were less dependent on worked examples as a resource for learning, and that students who only sporadically used worked examples achieved higher test scores.
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
NSTA15: Performance-Based Portfolio Assessment of the NGSSChris Ludwig
Slide deck for Chris Ludwig's presentation at NSTA15 in Chicago: Student Managed Portfolios: Performance-Based Alternatives to Standardized Tests for the NGSS
This course is specially designed for healthcare professionals and will provide the knowledge, skills, and competencies required for delivering effective teaching using instructional design and educational technology. Participants will actively understand and apply the instructional design process, from analysis through evaluation, and engage in authentic instructional design activities in their subject area.
Relating Instructional Materials Use to Student Achievement Using Validated M...Amy Cassata, PhD
Amy Cassata, PhD (Outlier Research & Evaluation, CEMSE, University of Chicago) presented this slide show at the Institute of Education Sciences Principal Investigator meeting on September 2, 2014. Findings report on data collected through IES Grant #R305A110621.
Session: "Mathematics and Science Assessment Showcase." Session Description: Research focusing on the development and validation of innovative mathematics and science assessments has increased in recent years. In this session, several NCER grantees will provide a brief synopsis of their work developing and validating assessments in mathematics and science. Participants will have an opportunity to learn about the different assessments, view demonstrations, and to interact with the presenters to discuss the assessments in greater depth.
LAK21 Data Driven Redesign of Tutoring Systems (Yun Huang)Yun Huang
This is the slides for our paper in LAK '21 conference:
Yun Huang, Nikki G. Lobczowski, J. Elizabeth Richey, Elizabeth A. McLaugh- lin, Michael W. Asher, Judith M. Harackiewicz, Vincent Aleven, and Kenneth R. Koedinger. 2021. A General Multi-method Approach to Data-Driven Re- design of Tutoring Systems. In LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12–16, 2021, Irvine, CA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3448139.3448155
Abstract: Analytics of student learning data are increasingly important for continuous redesign and improvement of tutoring systems and courses. There is still a lack of general guidance on converting analytics into better system design, and on combining multiple methods to maximally improve a tutor. We present a multi-method approach to data-driven redesign of tutoring systems and its empirical evaluation. Our approach systematically combines existing and new learning analytics and instructional design methods. In particular, our methods involve identifying difficult skills and creating focused tasks for learning these difficult skills effectively following content redesign strategies derived from analytics. In our past work, we applied this approach to redesigning an algebraic modeling unit and found initial evidence of its effectiveness. In the current work, we extended this approach and applied it to redesigning two other tutor units in addition to a second iteration of redesigning the previously redesigned unit. We conducted a one-month classroom experiment with 129 high school students. Compared to the origi- nal tutor, the redesigned tutor led to significantly higher learning outcomes, with time mainly allocated to focused tasks rather than original full tasks. Moreover, it reduced over- and under-practice, yielded a more effective practice experience, and selected skills progressing from easier to harder to a greater degree. Our work provides empirical evidence of the effectiveness and generality of a multi-method approach to data-driven instructional redesign.
ALT-C 2019 Jisc curriculum analytics - full set of slidesPaul Bailey
A deep dive into student data to discover curriculum insights
Authors: Paul Bailey, Niall Sclater, Michael Webb, Alan Paull, and Scott Wilson
A full set of slides around curriculum analytics.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
2. Kostas Perifanos, Learner Analytics & Data Science
EdTech Meetup, July 10, 2014
Introduction to
Adaptive Learning
3. Kostas Perifanos, Learner Analytics & Data Science
Aim:
“[...] Computers adapt the presentation of educational material according to student’s
learning needs, as indicated by their responses to questions and tasks.” [wikipedia]
● Knowledge inference: Measure what a student knows at a specific time
● Scientific understanding of Learning:
● Ignore domain differences and focus on the kind of knowledge being taught
● Knowledge Components - KC’s
Introduction to Adaptive Learning
4. Kostas Perifanos, Learner Analytics & Data Science
Abstracting the educational material:
“A knowledge component is a description of a mental structure or process that a learner
uses, alone or in combination with other knowledge components, to accomplish steps
in a task or a problem.” [Pittsburgh Science for Learning Center]
http://www.learnlab.org/research/wiki/index.php/Knowledge_component
http://www.learnlab.org/opportunities/summer/presentations/2010/2010-pslc-summer-
school%20Geoff%20Gordon.pdf
Introduction to Adaptive Learning
5. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
● KC: Anything a student can know/do:
● Skill
● Fact
● Concept
● Principle etc
● KC’s can be:
● Low Level Things
● High Level things
● Motivational Things etc
6. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
If we can measure knowledge:
● We can make it better
● We can provide tutors with meaningful feedback
● We can make automated decisions
… thus, we can implement “Adaptive Learning” solutions
● Knowledge Inference / Latent Knowledge Estimation
● Latent = Not directly observable (measurable)
7. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
How to infer knowledge?
We can look at student performance over time
Build and evaluate models
Two views of KC’s
Statistical Model: what latent factors in a student/step explain observed data
Cognitive Model: what is the structure of the internal reasoning system students use
to solve problems
8. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
Statistical/Machine Learning Vision
Given a cognitive model:
Evaluate the model
Evaluate the student
Provide feedback
Pure ML approaches are not heavily used just yet, human interaction and interpretation is
required
9. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
Common Models
● Bayesian Knowledge Tracing
● Performance Factors Analysis
● Item Response Theory - Rasch Model
● Additive Factor Model (AFM)
Two main approaches:
- Does student X knows skill K? [Knowledge Tracing]
- Calculate the probability of a correct answer given student and skill [IRT, AFM, PFA]
P(correct| features of student and step at time t)
10. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
Bayesian Knowledge Tracing
How well a student knows a specific skill/component at a specific time, based on their
performance
Each item corresponds to a single skill
Measure skill/KC knowledge at a specific time
Two learning parameters [P(L), P(T)]
Two performance parameters [P(G), P(S)]
P(L): Probability the skill is already known
P(T): Probability the skill will be learned
P(G): Probability of correct guessing [I don’t know the skill and I am guessing]
P(S): Probability of slip [I know the skill but I made a mistake]
Model fitting: Expectation Maximization [Hidden Markov Model]
11. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
Item Response Theory
Model probability of correct as function of student knowledge level and item difficulty
Additive Factor Model
Model probability of correct as function of student knowledge level and item difficulty,
but also take into account skill learning rate.
Each item has a KC and this determines the difficulty of the item.
Learning rates: How fast students are learning specific skills
12. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
Performance Factors Analysis
Measure the latent skill as the probability of correctness the next time we
encounter this skill
Multiple KC’s per item
Parametrized skills [success learning rate, failure learning rate] and item
difficulty
Take into account success and failure
13. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
What if we don’t know the KC’s?
Principal Components Analysis (PCA):
Factor student-step data in “eigenskills” to obtain most important “interactions”
Good at making predictions
Features are not easily interpretable
14. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
Overview:
● Knowledge tracing
● Directly model “Does the student know X”, suitable for good instructions
● Knowledge is latent, harder to fit but more flexible
● AFM
● Used for refining KC models, detect a bad KC
● Knowledge is observable [Fully Markov Model]
● assumes all students accumulate knowledge in the same manner and ignores the
correctness of their individual responses
● PFA: LR model, similar to AFM but take into account individual responses (Successes
vs Failures)
16. Kostas Perifanos, Learner Analytics & Data Science
Introduction to Adaptive Learning
(Warning: Next presentation will have all the maths and implementation details)
Thank you
