Keynote presentation of Yannis Dimitriadis at Intelligent Tutoring Systems 2022: Human-Centered Learning Analytics: Designing for balanced human and computational agency
This document discusses human-centered learning analytics and the importance of teacher agency in designing learning analytics solutions. It addresses two dilemmas: 1) existing learning analytics solutions often ignore teacher agency and orchestration, and 2) artificial intelligence agents using learning analytics may not be transparent, trustworthy, responsible or ethical from a student perspective. The talk will discuss these dilemmas, models for human-AI complementarity that augment teachers, and principles for human-centered learning analytics that involve teachers and students in the design process. The goal is to design learning analytics solutions that consider both teacher and student agency through a human-centered approach.
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...Yannis
Although Artificial Intelligence (AI) and Learning Analytics (LA) have shown their potential in Education, stakeholders’ agency seems to be threatened. On the other hand, multiple issues regarding FATE (Fairness, Accountability, Transparency and Ethics) have been raised when AI or LA-based solutions are designed and implemented. These issues have been especially acute since the emergence of Large Language Models and Generative AI.
This talk discusses the quest for an optimal balance between human and computational agents, when LA tools and services are employed in a Technology Enhanced Learning (TEL) ecosystem. Through the discussion of relevant conceptual models and examples, it argues for Human-Centered Learning Analytics (HCLA) and Human-Centered Artificial Intelligence (HCAI) approaches, where agency and FATE principles are essential design parameters.
The talk focuses especially on LA/AI solutions that may position teachers as designers of effective interventions and orchestration actions. Selected Human-Centered Design (HCD) principles are discussed and illustrated, and directions for future research and development are formulated to overcome the main obstacles for adoption of human-centered approaches for LA and AI in education.
Are we currently moving from the age of mobolism to age of artificail intelli...Jari Laru
The 13th annual International Technology, Education and Development Conference, INTED2019,IValencia (Spain). 11th-13th of March, 2019. Special Learning Technology Accelerator (Lea) Horizon 2020 project session: Innovation procurement to steer user-driven innovations for digital learning.
1) The document discusses predictions for the future of educational technology (edtech) in 2030 based on a presentation by Dr. Jari Laru.
2) It outlines near-term edtech developments that are already available but not widely used, such as programming/robotics and learning management systems.
3) The document also discusses not-so-distant future edtech research trends and projects focusing on adaptive learning materials, smart learning environments, multimodal data collection and learning analytics.
4) Pedagogical agents and educational robots are presented as another potential edtech development in the not-so-distant future.
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»eMadrid network
1. The document discusses three conceptualizations of multimodal learning analytics (MMLA): MMLA to automate human tasks, augment teaching and learning practices, and as a research methodology.
2. It examines what modalities of data are used in MMLA, including video/audio data, eye tracking data, physiological sensors, and location sensing. Machine learning has been applied to MMLA tasks like classifying collaboration.
3. Challenges of MMLA include connecting findings to learning theory, addressing ethics concerns like privacy and surveillance, and determining what behaviors are considered good or bad in education. Students have mixed reactions to being analyzed by MMLA.
The document discusses teaching and learning with technology in the 21st century. It argues that technology alone is not enough and that the pedagogy used is key. It advocates shifting education's focus from knowledge to skills like collaboration, critical thinking, and problem solving. New skills are needed like responsibility, reliability and integrity in the digital age. Educational tools discussed include educational games, gamification, robots, multitouch tables, and their benefits. The document concludes with references supporting technology integration best practices.
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...Jari Laru
The document discusses the future possibilities and challenges of using digital tools in education from three perspectives:
1) Today, where educational institutions follow current practices in the field. 2) Tomorrow, looking at predictions from research about areas like adaptive learning, smart learning environments, and educational data mining. 3) A distant future, where the possibilities are unknown since technology is changing rapidly. Overall, the document emphasizes that technology should be used to support new educational designs that help address 21st century skills, rather than seeing it as the answer on its own.
This document discusses human-centered learning analytics and the importance of teacher agency in designing learning analytics solutions. It addresses two dilemmas: 1) existing learning analytics solutions often ignore teacher agency and orchestration, and 2) artificial intelligence agents using learning analytics may not be transparent, trustworthy, responsible or ethical from a student perspective. The talk will discuss these dilemmas, models for human-AI complementarity that augment teachers, and principles for human-centered learning analytics that involve teachers and students in the design process. The goal is to design learning analytics solutions that consider both teacher and student agency through a human-centered approach.
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...Yannis
Although Artificial Intelligence (AI) and Learning Analytics (LA) have shown their potential in Education, stakeholders’ agency seems to be threatened. On the other hand, multiple issues regarding FATE (Fairness, Accountability, Transparency and Ethics) have been raised when AI or LA-based solutions are designed and implemented. These issues have been especially acute since the emergence of Large Language Models and Generative AI.
This talk discusses the quest for an optimal balance between human and computational agents, when LA tools and services are employed in a Technology Enhanced Learning (TEL) ecosystem. Through the discussion of relevant conceptual models and examples, it argues for Human-Centered Learning Analytics (HCLA) and Human-Centered Artificial Intelligence (HCAI) approaches, where agency and FATE principles are essential design parameters.
The talk focuses especially on LA/AI solutions that may position teachers as designers of effective interventions and orchestration actions. Selected Human-Centered Design (HCD) principles are discussed and illustrated, and directions for future research and development are formulated to overcome the main obstacles for adoption of human-centered approaches for LA and AI in education.
Are we currently moving from the age of mobolism to age of artificail intelli...Jari Laru
The 13th annual International Technology, Education and Development Conference, INTED2019,IValencia (Spain). 11th-13th of March, 2019. Special Learning Technology Accelerator (Lea) Horizon 2020 project session: Innovation procurement to steer user-driven innovations for digital learning.
1) The document discusses predictions for the future of educational technology (edtech) in 2030 based on a presentation by Dr. Jari Laru.
2) It outlines near-term edtech developments that are already available but not widely used, such as programming/robotics and learning management systems.
3) The document also discusses not-so-distant future edtech research trends and projects focusing on adaptive learning materials, smart learning environments, multimodal data collection and learning analytics.
4) Pedagogical agents and educational robots are presented as another potential edtech development in the not-so-distant future.
2022_11_11 «The promise and challenges of Multimodal Learning Analytics»eMadrid network
1. The document discusses three conceptualizations of multimodal learning analytics (MMLA): MMLA to automate human tasks, augment teaching and learning practices, and as a research methodology.
2. It examines what modalities of data are used in MMLA, including video/audio data, eye tracking data, physiological sensors, and location sensing. Machine learning has been applied to MMLA tasks like classifying collaboration.
3. Challenges of MMLA include connecting findings to learning theory, addressing ethics concerns like privacy and surveillance, and determining what behaviors are considered good or bad in education. Students have mixed reactions to being analyzed by MMLA.
The document discusses teaching and learning with technology in the 21st century. It argues that technology alone is not enough and that the pedagogy used is key. It advocates shifting education's focus from knowledge to skills like collaboration, critical thinking, and problem solving. New skills are needed like responsibility, reliability and integrity in the digital age. Educational tools discussed include educational games, gamification, robots, multitouch tables, and their benefits. The document concludes with references supporting technology integration best practices.
Digitaaliset välineet opetuksessa ja oppimisessa opettajankoulutuksen konteks...Jari Laru
The document discusses the future possibilities and challenges of using digital tools in education from three perspectives:
1) Today, where educational institutions follow current practices in the field. 2) Tomorrow, looking at predictions from research about areas like adaptive learning, smart learning environments, and educational data mining. 3) A distant future, where the possibilities are unknown since technology is changing rapidly. Overall, the document emphasizes that technology should be used to support new educational designs that help address 21st century skills, rather than seeing it as the answer on its own.
The evolution and adoption of Learning Analytics (LA) participates in the debate about the ethical challenges associated to technological advancement and the need to provide responsible technology. This debate in the field of educational technology focuses on the tension between the potential of LA to achieve more effective education and its impact on human behavior and well-being. In this talk I will present examples of solutions based on learning analytics proposed in the TIDE research group of Pompeu Fabra University - Barcelona (https://www.upf.edu/web/tide) that try to meet requirements of human-centred design, support for human agency, transparency, or human well-being. Examples include systems with LA components to support the design and orchestration of active learning activities, especially collaborative learning activities.
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...Bart Rienties
This keynote will help you:
-Understand where to start with learning analytics
-Understand how to effectively support your staff to use data
-Critically review whether learning analytics is something for your organisation
https://www.terrapinn.com/exhibition/edutech-europe/speaker-bart-RIENTIES.stm
Cognitive Computing and Education and Learningijtsrd
Its enormous potential in learning spurs Cognitive Computing. The overreaching purpose here is to devise computational frameworks to help us learn better by exploiting the learning process and activities. The research challenge recognized the broad spectrum of human learning, the complex and not fully understood human learning process, and various learning factors, such as pedagogy, technology, and social elements. From the theoretical point of view, Cognitive Computing could replace existing calculators in many applications. This paper focuses on applying data mining and learning analytics, clustering student modeling, and predicting student performance when involved in the education field with possible approaches. Latifa Rahman "Cognitive Computing and Education and Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49783.pdf Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/education/49783/cognitive-computing-and-education-and-learning/latifa-rahman
This document summarizes a presentation on learning design technologies that support collective and inclusive approaches to education. It discusses how learning design can be used to promote equal opportunities for participation, guide the design of learning activities, enable collective efforts in co-designing learning, and regulate learning processes. It provides examples of how collaborative learning scripts, authoring tools, analytics layers, and orchestration technologies like PyramidApp can support these goals. Current and future work focuses on designing AI and technologies to be responsible and protect children's rights.
Efficient and effective mobile collaborative learningdavinia.hl
Keynote at mLearn22 https://www.iamlearn.org/mlearn/
In this talk I will summarize research results leading to practical implications in the achievement of efficient and effective (enjoyable, appealing) collaborative learning, both from the perspective of learners and teachers. In particular, I will focus on how technology can support the design and orchestration of mobile collaborative learning scenarios. The technology presented will include authoring tools, teaching community platforms, enactment systems, orchestration dashboards and data-driven intervention based on learning analytics. I will also discuss synergies between technological solutions emphasizing human-in-control and machine-in-control perspectives. During the talk, participants will be able to experience some notions covered by interacting using the PyramidApp tool.
Digitaalinen tulevaisuus 2030 – kuinka ”tukiäly” tukee ihmisten arkea, oppimi...Jari Laru
(1) The document discusses how artificial intelligence and digital technologies will impact education and work in the future. (2) It describes current applications of AI such as personalized learning environments and interactive content creation. (3) The distant future possibilities discussed include AI-generated art and music, AI to support learning for those with special needs, and AI to assist with information retrieval and tasks at work. The presentation emphasizes that technology should be used to support stable educational goals and new designs for learning.
Keynote Presentation: Implementing learning analytics and learning design at ...Bart Rienties
The University of the Roller Coaster
How can Higher Education function in a world struggling to save itself from climate change, pandemics and war? How can it drive innovation and shape the future as the pace of technological change constantly increases? How can it re-invent itself to respond imaginatively to the new challenges facing humanity?
We are living in an uncertain, unpredictable world with no “back to normal” any more. So, how can we re-imagine higher education when nothing can be taken for granted? What kind of technologies can help universities to adapt? What lessons can we learn from recent successes and failures? What 'best practice' examples point the way into the future? How can we shape the development of institutions, so that they are neither “ivory towers” nor “competence factories"? How can we encourage future-oriented universities in which both pedagogy and research are fit for the challenges ahead?
In the Academic Plenary, our experts will examine the threats and opportunities facing higher education today and ask how we can design new approaches that prepare staff and students to thrive in the University of the Roller Coaster.
Webinar: Learning Informatics Lab, University of Minnesota
Replay the talk: https://youtu.be/dcJZeDIMr2I
Learning Informatics
AI • Analytics • Accountability • Agency
Simon Buckingham Shum
Professor of Learning Informatics
Director, Connected Intelligence Centre
University of Technology Sydney
Abstract:
“Health Informatics”. “Urban Informatics”. “Social Informatics”. Informatics offers systemic ways of analyzing and designing the interaction of natural and artificial information processing systems. In the context of education, I will describe some Learning Informatics lenses and practices which we have developed for co-designing analytics and AI with educators and students. We have a particular focus on closing the feedback loop to equip learners with competencies to navigate a complex, uncertain future, such as critical thinking, professional reflection and teamwork. En route, we will touch on how we build educators’ trust in novel tools, our design philosophy of “embracing imperfection” in machine intelligence, and the ways that these infrastructures embody values. Speaking from the perspective of leading an institutional innovation centre in learning analytics, I hope that our experiences spark productive reflection around as the UMN Learning Informatics Lab builds its program.
Biography:
Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, where he serves as inaugural director of the Connected Intelligence Centre. CIC is a transdisciplinary innovation centre, using analytics to provide new insights for university teams, with particular expertise in educational data science. Simon’s career-long fascination with software’s ability to make thinking visible has seen him active in communities including Computer-Supported Cooperative Work, Hypertext, Design Rationale, Scholarly Publishing, Semantic Web, Computational Argumentation, Educational Technology and Learning Analytics. The challenge of visualizing contested knowledge has produced several books: Visualizing Argumentation, Knowledge Cartography, and Constructing Knowledge Art. He has been active over the last decade in shaping the field of Learning Analytics, co-founding the Society for Learning Analytics Research, and catalyzing several strands: Social Learning Analytics, Discourse Analytics, Dispositional Analytics and Writing Analytics. http://Simon.BuckinghamShum.net
«Assessment of Digital Resources use in Education - Anatomy of Digital Resources in Learning Generation»
languages, civics curricula, anatomy of different digital tools web 2.0, assessment
E-Learning in Maths - Research, practical tips and discussionStephen McConnachie
Plenary presentation from conference on 23rd October 2014. Overview of relevant research, practical frameworks for designing and evaluating learning activities (TPACK and the Activity Types taxonomy), and a quick look at the SAMR model.
Educational Technology - opportunities and pitfalls How to make the most use...Bart Rienties
The keynote presentation covered opportunities and limitations of educational technology based on learning analytics research. It included three research exemplars: 1) a study that found students' self-reported internet searching skills did not match their actual online behavior, 2) a randomized study showing how internationalized course content can encourage participation in diverse groups, and 3) a project linking multiple datasets across 150+ modules to predict student outcomes. The talk concluded by emphasizing the need to consider ethics and standardization as more educational data becomes available and harvested for learning analytics.
A learning scientist approach to modeling human cognition in individual and c...Margarida Romero
A learning scientist approach to modeling human cognition in individual and collaborative problem solving tasks. 12 février 2021. Mini-cours. NeuroMod Institute. Université Côte d'Azur.
Emerging technologies and Changing Teaching and Learning PracticesDaniela Gachago
This document discusses emerging technologies and changing teaching and learning practices in higher education. It notes challenges in higher education including teaching outdated skills and lack of teacher involvement in innovation. Emerging technologies promise benefits but are seldom used transformatively. The document outlines a South African project studying innovative pedagogical practices using emerging technologies and lessons learned. Case studies showed technologies can enable authentic learning when used to engage students in meaningful, collaborative tasks. Themes included the importance of passionate educators over institutional support and focusing on meaningful learning in authentic contexts.
Reconsidering digital education through a theory of practice Cristina Costa
Through a Bourdieuian lens, the document discusses digital technologies in higher education. It summarizes key Bourdieu concepts like habitus, capital, and field as lenses to understand student experiences with digital learning. Specifically, it examines how habitus, represented through student dispositions, interacts with digital cultures and literacies within the field of higher education. The document advocates combining Bourdieu with other theories for a flexible research approach to challenge assumptions and understand student practices.
The document discusses the use of digital resources in education for new generations of digital native students. It notes that digital technologies are now ubiquitous and students often learn outside the classroom using the internet. The objective is to analyze how this new generation interacts with and responds to new ways of learning using digital resources both in and outside the classroom. It is important that digital resources have quality, creativity, and encourage innovative thinking. The impact of digital resources on students' cognitive skills and learning expectations is an important issue to consider. Digital resources can help bridge formal and informal learning when students are able to apply their skills and relate their schoolwork to real-world contexts. The role of both teachers and students is crucial for providing feedback on the quality of digital
The document discusses issues around digital education, including both promises and threats. It examines perspectives that see technology as either driving changes or being adopted by users. It also discusses seeing the human and non-human as entangled rather than separate. Case studies look at how algorithms and automated systems shape participation and knowledge. The document calls for moving beyond questions of effectiveness to consider what we want from digital education.
DigiReady+ Implementando un marco basado en datos para medir la preparación ...Yannis
El documento presenta el proyecto DigiReady+, cuyo objetivo es definir un marco de referencia (DR+) y desarrollar una plataforma (UDReady) para medir la preparación digital de las universidades europeas basándose en análisis de datos institucionales. El marco DR+ contiene siete dimensiones para evaluar la preparación digital e indicadores agrupados en temas. La plataforma UDReady calculará la puntuación DR+, ofrecerá informes y recomendaciones personalizadas. El proyecto busca validar el en
The doctoral thesis trajectory has been often characterized as a “long and windy road” or a journey to “Ithaka”, suggesting the promises and challenges of this journey of initiation to research.
The doctoral candidates need to complete such journey
preserving and even enhancing their wellbeing,
overcoming the many challenges through resilience, while keeping
high standards of ethics and
scientific rigor.
This talk will provide a personal account of lessons learnt and recommendations from a senior researcher over his 30+ years of doctoral supervision and care for doctoral students.
Specific attention will be paid on the special features of the
(interdisciplinary doctoral research in Technology Enhanced Learning (TEL),
the eventual convergence of mindsets and epistemological traditions in Information and Communications Technologies (ICT) and human-oriented learning, educational or social sciences, as well as
the specific challenges posed by the human-oriented features of the TEL field.
The evolution and adoption of Learning Analytics (LA) participates in the debate about the ethical challenges associated to technological advancement and the need to provide responsible technology. This debate in the field of educational technology focuses on the tension between the potential of LA to achieve more effective education and its impact on human behavior and well-being. In this talk I will present examples of solutions based on learning analytics proposed in the TIDE research group of Pompeu Fabra University - Barcelona (https://www.upf.edu/web/tide) that try to meet requirements of human-centred design, support for human agency, transparency, or human well-being. Examples include systems with LA components to support the design and orchestration of active learning activities, especially collaborative learning activities.
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...Bart Rienties
This keynote will help you:
-Understand where to start with learning analytics
-Understand how to effectively support your staff to use data
-Critically review whether learning analytics is something for your organisation
https://www.terrapinn.com/exhibition/edutech-europe/speaker-bart-RIENTIES.stm
Cognitive Computing and Education and Learningijtsrd
Its enormous potential in learning spurs Cognitive Computing. The overreaching purpose here is to devise computational frameworks to help us learn better by exploiting the learning process and activities. The research challenge recognized the broad spectrum of human learning, the complex and not fully understood human learning process, and various learning factors, such as pedagogy, technology, and social elements. From the theoretical point of view, Cognitive Computing could replace existing calculators in many applications. This paper focuses on applying data mining and learning analytics, clustering student modeling, and predicting student performance when involved in the education field with possible approaches. Latifa Rahman "Cognitive Computing and Education and Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49783.pdf Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/education/49783/cognitive-computing-and-education-and-learning/latifa-rahman
This document summarizes a presentation on learning design technologies that support collective and inclusive approaches to education. It discusses how learning design can be used to promote equal opportunities for participation, guide the design of learning activities, enable collective efforts in co-designing learning, and regulate learning processes. It provides examples of how collaborative learning scripts, authoring tools, analytics layers, and orchestration technologies like PyramidApp can support these goals. Current and future work focuses on designing AI and technologies to be responsible and protect children's rights.
Efficient and effective mobile collaborative learningdavinia.hl
Keynote at mLearn22 https://www.iamlearn.org/mlearn/
In this talk I will summarize research results leading to practical implications in the achievement of efficient and effective (enjoyable, appealing) collaborative learning, both from the perspective of learners and teachers. In particular, I will focus on how technology can support the design and orchestration of mobile collaborative learning scenarios. The technology presented will include authoring tools, teaching community platforms, enactment systems, orchestration dashboards and data-driven intervention based on learning analytics. I will also discuss synergies between technological solutions emphasizing human-in-control and machine-in-control perspectives. During the talk, participants will be able to experience some notions covered by interacting using the PyramidApp tool.
Digitaalinen tulevaisuus 2030 – kuinka ”tukiäly” tukee ihmisten arkea, oppimi...Jari Laru
(1) The document discusses how artificial intelligence and digital technologies will impact education and work in the future. (2) It describes current applications of AI such as personalized learning environments and interactive content creation. (3) The distant future possibilities discussed include AI-generated art and music, AI to support learning for those with special needs, and AI to assist with information retrieval and tasks at work. The presentation emphasizes that technology should be used to support stable educational goals and new designs for learning.
Keynote Presentation: Implementing learning analytics and learning design at ...Bart Rienties
The University of the Roller Coaster
How can Higher Education function in a world struggling to save itself from climate change, pandemics and war? How can it drive innovation and shape the future as the pace of technological change constantly increases? How can it re-invent itself to respond imaginatively to the new challenges facing humanity?
We are living in an uncertain, unpredictable world with no “back to normal” any more. So, how can we re-imagine higher education when nothing can be taken for granted? What kind of technologies can help universities to adapt? What lessons can we learn from recent successes and failures? What 'best practice' examples point the way into the future? How can we shape the development of institutions, so that they are neither “ivory towers” nor “competence factories"? How can we encourage future-oriented universities in which both pedagogy and research are fit for the challenges ahead?
In the Academic Plenary, our experts will examine the threats and opportunities facing higher education today and ask how we can design new approaches that prepare staff and students to thrive in the University of the Roller Coaster.
Webinar: Learning Informatics Lab, University of Minnesota
Replay the talk: https://youtu.be/dcJZeDIMr2I
Learning Informatics
AI • Analytics • Accountability • Agency
Simon Buckingham Shum
Professor of Learning Informatics
Director, Connected Intelligence Centre
University of Technology Sydney
Abstract:
“Health Informatics”. “Urban Informatics”. “Social Informatics”. Informatics offers systemic ways of analyzing and designing the interaction of natural and artificial information processing systems. In the context of education, I will describe some Learning Informatics lenses and practices which we have developed for co-designing analytics and AI with educators and students. We have a particular focus on closing the feedback loop to equip learners with competencies to navigate a complex, uncertain future, such as critical thinking, professional reflection and teamwork. En route, we will touch on how we build educators’ trust in novel tools, our design philosophy of “embracing imperfection” in machine intelligence, and the ways that these infrastructures embody values. Speaking from the perspective of leading an institutional innovation centre in learning analytics, I hope that our experiences spark productive reflection around as the UMN Learning Informatics Lab builds its program.
Biography:
Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, where he serves as inaugural director of the Connected Intelligence Centre. CIC is a transdisciplinary innovation centre, using analytics to provide new insights for university teams, with particular expertise in educational data science. Simon’s career-long fascination with software’s ability to make thinking visible has seen him active in communities including Computer-Supported Cooperative Work, Hypertext, Design Rationale, Scholarly Publishing, Semantic Web, Computational Argumentation, Educational Technology and Learning Analytics. The challenge of visualizing contested knowledge has produced several books: Visualizing Argumentation, Knowledge Cartography, and Constructing Knowledge Art. He has been active over the last decade in shaping the field of Learning Analytics, co-founding the Society for Learning Analytics Research, and catalyzing several strands: Social Learning Analytics, Discourse Analytics, Dispositional Analytics and Writing Analytics. http://Simon.BuckinghamShum.net
«Assessment of Digital Resources use in Education - Anatomy of Digital Resources in Learning Generation»
languages, civics curricula, anatomy of different digital tools web 2.0, assessment
E-Learning in Maths - Research, practical tips and discussionStephen McConnachie
Plenary presentation from conference on 23rd October 2014. Overview of relevant research, practical frameworks for designing and evaluating learning activities (TPACK and the Activity Types taxonomy), and a quick look at the SAMR model.
Educational Technology - opportunities and pitfalls How to make the most use...Bart Rienties
The keynote presentation covered opportunities and limitations of educational technology based on learning analytics research. It included three research exemplars: 1) a study that found students' self-reported internet searching skills did not match their actual online behavior, 2) a randomized study showing how internationalized course content can encourage participation in diverse groups, and 3) a project linking multiple datasets across 150+ modules to predict student outcomes. The talk concluded by emphasizing the need to consider ethics and standardization as more educational data becomes available and harvested for learning analytics.
A learning scientist approach to modeling human cognition in individual and c...Margarida Romero
A learning scientist approach to modeling human cognition in individual and collaborative problem solving tasks. 12 février 2021. Mini-cours. NeuroMod Institute. Université Côte d'Azur.
Emerging technologies and Changing Teaching and Learning PracticesDaniela Gachago
This document discusses emerging technologies and changing teaching and learning practices in higher education. It notes challenges in higher education including teaching outdated skills and lack of teacher involvement in innovation. Emerging technologies promise benefits but are seldom used transformatively. The document outlines a South African project studying innovative pedagogical practices using emerging technologies and lessons learned. Case studies showed technologies can enable authentic learning when used to engage students in meaningful, collaborative tasks. Themes included the importance of passionate educators over institutional support and focusing on meaningful learning in authentic contexts.
Reconsidering digital education through a theory of practice Cristina Costa
Through a Bourdieuian lens, the document discusses digital technologies in higher education. It summarizes key Bourdieu concepts like habitus, capital, and field as lenses to understand student experiences with digital learning. Specifically, it examines how habitus, represented through student dispositions, interacts with digital cultures and literacies within the field of higher education. The document advocates combining Bourdieu with other theories for a flexible research approach to challenge assumptions and understand student practices.
The document discusses the use of digital resources in education for new generations of digital native students. It notes that digital technologies are now ubiquitous and students often learn outside the classroom using the internet. The objective is to analyze how this new generation interacts with and responds to new ways of learning using digital resources both in and outside the classroom. It is important that digital resources have quality, creativity, and encourage innovative thinking. The impact of digital resources on students' cognitive skills and learning expectations is an important issue to consider. Digital resources can help bridge formal and informal learning when students are able to apply their skills and relate their schoolwork to real-world contexts. The role of both teachers and students is crucial for providing feedback on the quality of digital
The document discusses issues around digital education, including both promises and threats. It examines perspectives that see technology as either driving changes or being adopted by users. It also discusses seeing the human and non-human as entangled rather than separate. Case studies look at how algorithms and automated systems shape participation and knowledge. The document calls for moving beyond questions of effectiveness to consider what we want from digital education.
Semelhante a yannis@its2022_20220701_final.pptx (20)
DigiReady+ Implementando un marco basado en datos para medir la preparación ...Yannis
El documento presenta el proyecto DigiReady+, cuyo objetivo es definir un marco de referencia (DR+) y desarrollar una plataforma (UDReady) para medir la preparación digital de las universidades europeas basándose en análisis de datos institucionales. El marco DR+ contiene siete dimensiones para evaluar la preparación digital e indicadores agrupados en temas. La plataforma UDReady calculará la puntuación DR+, ofrecerá informes y recomendaciones personalizadas. El proyecto busca validar el en
The doctoral thesis trajectory has been often characterized as a “long and windy road” or a journey to “Ithaka”, suggesting the promises and challenges of this journey of initiation to research.
The doctoral candidates need to complete such journey
preserving and even enhancing their wellbeing,
overcoming the many challenges through resilience, while keeping
high standards of ethics and
scientific rigor.
This talk will provide a personal account of lessons learnt and recommendations from a senior researcher over his 30+ years of doctoral supervision and care for doctoral students.
Specific attention will be paid on the special features of the
(interdisciplinary doctoral research in Technology Enhanced Learning (TEL),
the eventual convergence of mindsets and epistemological traditions in Information and Communications Technologies (ICT) and human-oriented learning, educational or social sciences, as well as
the specific challenges posed by the human-oriented features of the TEL field.
Keynote talk at CollabTech2022 (November 9, 2022):
Design and orchestration of technology-enhanced collaborative learning can be very challenging for teachers or even instructional designers. This keynote presentation deals with design for effective and efficient collaborative learning, and how teachers as designers and orchestrators may be supported in complex ecosystems.
We present the main challenges and solutions regarding conceptual and technological tools which may be developed, building on, and adapting to existing design knowledge.
The talk will provide an overview of patterns, approaches, tools, and systems that should respect teachers’ agency while taking advantage of complex computational approaches, typically based on artificial intelligence.
We pay special attention to recent research on how learning analytics solutions may be designed and implemented using human-centered approaches, and how socially shared regulated learning may be better supported.
Several illustrating examples will be shown drawing on the literature and the research work of the presented during the last 25 years.
Some prominent pending issues will be posed that may guide future research in supporting teachers as designers and orchestrators.
Este documento presenta principios de diseño centrado en la persona para analítica de aprendizaje orientada a la acción. Discute la importancia de alinear la analítica de aprendizaje con el diseño de aprendizaje e involucrar a los docentes y estudiantes. También enfatiza el uso de teorías educativas para guiar el diseño e implementación de soluciones de analítica de aprendizaje. Finalmente, presenta un estudio de caso que ilustra cómo aplicar estos principios para desarrollar una herramienta que guíe
Designing for effective and efficient pedagogical interventions and orchestration in complex Technology-Enhanced Learning (TEL) ecosystems is an increasingly challenging issue.
In spite of the significant potential of Learning Analytics (LA) research, it is still unclear how can LA be designed to position teachers as designers of effective interventions and orchestration actions.
This talk argues for Human-Centered Design (HCD) and orchestration of actionable learning analytics. It provides a review of needs and existing approaches for HCD in LA is provided, and it proposes three HCD principles for LA solutions, i.e., agentic positioning of teachers and other stakeholders; integration of the learning design cycle and the LA design process; and reliance on educational theories to guide the LA solution design and implementation.
The HCD principles are illustrated and discussed through two case studies in authentic learning contexts.
Finally, some directions for future research and development are formulated to overcome the main obstacles for adoption of HCD for LA.
This presentation discusses aligning learning design and learning analytics using a human-centered design approach. It provides an overview of the connections between learning analytics and learning design, and how learning theory can inform learning analytics. It then describes a longitudinal study that developed a learning analytics tool called the Teacher Action Planner, which was grounded in learning theory and aligned with the learning design and platform. The study found that bringing teachers into the design process and basing the tool on learning theory and design principles helped optimize the learning design based on analytics. However, longer term studies are still needed to fully understand the impacts on teaching and learning.
This document summarizes a seminar on research methodologies in technology enhanced learning. It discusses various topics related to doctoral research including defining research questions, literature reviews, methodologies, evaluation processes, research communities and paradigms. Examples from past student theses are provided to demonstrate mixed methods approaches, iterative design processes, and lessons learned regarding defining problems, collecting and analyzing data, and engaging with peer review.
Supporting teachers as designers: (Some) Research threads at GSIC/EMICYannis
Some current research threads at GSIC/EMIC: (1) Design for Learning, (2) Some systems: ILDE and GluePS-AR, (3) Aligning Learning Analytics, Design for Learning, Orchestration
Design and orchestration of CSCL educational scenarios is still a challenge for teachers and instructional designers.
Conceptual and technological support to teachers as designers is essential for a sustainable, effective and efficient adoption of innovative pedagogical approaches in increasing complex technology-enhanced learning ecosystems.
This talk presents an overview of patterns, software architectures and environments that support design for learning, drawn from proposals made by the GSIC/EMIC group, together with illustrative examples.
Finally, we discuss some issues regarding effective orchestration actions and pedagogical interventions based on learning analytics and aligned with the design of the educational scenarios.
This document discusses aligning design for learning (D4L) with learning analytics (LA) in technology-enhanced learning ecosystems. It proposes that D4L knowledge can be captured through conceptual tools like principles, patterns and processes. D4L forms part of teacher inquiry and should inform and be informed by LA through teaching analytics. Multiple frameworks could explain the relationships between D4L, LA, the learning environment and stakeholders like teachers. Bringing these perspectives together could help support teachers as designers and orchestrators through sustainable systems.
Patterns, approaches and systems to support teachers in designing fortechnol...Yannis
Technology-enhanced collaborative learning can be very challenging for teachers and instructional designers. This seminar deals with design for learning in such contexts showing how pedagogical collaborative learning flow and atomic patterns can be employed in order to promote effective and efficient learning.
Moreover, the talks presents the “In media res framework” and the associated forward-oriented approach of design for
learning, as well as ICT environments and systems that may support teachers. Examples are provided that illustrate crucial elements that can be designed for orchestration, awareness, analytics, reflection and redesign. Finally, some current and future lines of research are presented with respect to the mutual connection between learning design and learning analytics in collaborative learning contexts.
Conferencia invitada de Yannis Dimitriadis "Diseñando para orquestrar situaciones CSCL", Seminario de eMadrid sobre Tecnologías para el Aprendizaje Colaborativo, Madrid, 15 de febrero de 2013
This document summarizes a lunchtime research seminar on orchestrating computer supported collaborative learning. It discusses conceptual tools like the "5+3" orchestration framework and multi-level patterns. It also discusses technological tools like the Web Collage learning design tool. The presentation evaluates these tools and their usefulness for supporting teachers in orchestrating technology-enhanced learning environments.
The document summarizes a workshop on digital ecosystems for collaborative learning that aims to help educators deploy CSCL scripts into mainstream virtual learning environments that integrate third-party web and augmented reality tools. Specifically, it seeks to integrate augmented reality into distributed learning environments along with VLEs and other web tools to help teachers sustainably use these tools in authentic collaborative learning classrooms. It presents a prototype and proof of concept using a jigsaw-based collaborative script, web and AR browsers, and geo-located web resources and 3D models to support the lifecycle of CSCL scripts and orchestrate reflected spaces across ubiquitous learning tools and devices.
Orchestrating collaborative technology-enhanced ecosystems: How to support te...Yannis
This document discusses tools for supporting teachers in orchestrating technology-enhanced learning ecosystems. It begins by introducing a conceptual framework called "5+3 aspects" for understanding orchestration and evaluating its completeness and usefulness. It then discusses "multi-level patterns" as conceptual tools to support teachers, including learning flow and assessment patterns. Technological tools are also important, including the Web Collage learning design tool which represents designs visually and allows incorporating patterns. The document emphasizes supporting teachers with both conceptual and technological tools to help orchestrate complex learning environments.
The pyramid pedagogical pattern and a sample associated educational scenario Yannis
The document describes an educational scenario based on the Pyramid pedagogical pattern. The pattern involves students facing an open-ended problem in small initial groups. The groups then join together in larger "supergroups" to discuss solutions and try to reach a consensus. This process repeats with the supergroups joining an even larger whole class discussion. The goal is for students to collaboratively solve the problem through multiple iterations of small group and larger group discussions.
Worksheet of deploying a learning design produced by Web Collage in MoodleYannis
The document discusses deploying educational designs created in Web Collage into Moodle using Glue!PS. It explains that Glue!PS imports data from Web Collage and allows modifying tool types and configuring tool reuse across activities. Upon generating a Moodle backup file, Glue!PS creates all required tool instances. The backup file can then be restored in Moodle to fully generate the course based on the original Web Collage design.
Deployment of a learning design produced by Web Collage at a Moolde VLEYannis
The document discusses deploying an educational scenario created in Web Collage into Moodle using the Glue!-PS tool. It involves clicking "Deploy in Glue!PS" in Web Collage, which transfers data to Glue!-PS. In Glue!-PS, the tools from Web Collage can be modified and a Moodle backup file is generated. This backup file can then be downloaded and restored in Moodle to create the course.
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Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Build applications with generative AI on Google CloudMárton Kodok
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Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
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Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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You can see the future first in San Francisco.
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The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
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Challenges of Nation Building-1.pptx with more important
yannis@its2022_20220701_final.pptx
1. Human-Centered Learning
Analytics: Designing for balanced
human and computational agency
Prof. Yannis Dimitriadis
GSIC/EMIC group
University of Valladolid, Spain
Intelligent Tutoring Systems 2022
July 1, 2022
3. Learning Analytics (LA)
in Technology Enhanced Learning (TEL)
Learning Analytics
“measurement, collection, analysis and reporting of data
about learners and their contexts, for purposes of
understanding and optimizing learning and the
environments in which it occurs”
n Most R&D - Innovation has been devoted to
– Mining patterns
– Deriving predictive models
– Providing dashboards
3
4. Predictive models with LA
(At-risk students)
4
Herodotou, C.; Hlosta, M.; Boroowa, Avinash; R., Bart; Zdrahal, Z. and Mangafa, C. (2019). Empowering online
teachers through predictive learning analytics. British Journal of Educational Technology, 50(6) pp. 3064–3079.
How does the predictive model work and how it was trained?
How were the data collected for this prediction model?
Who was involved in its design and who can use the data?
5. Pattern mining using LA
(Detection of learning strategies)
5
J. B. J. Huang, A. Y. Q. Huang, O. H. T. Lu and S. J. H. Yang, "Exploring Learning Strategies by Sequence Clustering
and Analysing their Correlation with Student's Engagement and Learning Outcome," 2021 International Conference
on Advanced Learning Technologies (ICALT), 2021, pp. 360-362, doi: 10.1109/ICALT52272.2021.00115.
How are the proxies for strategies DEFINED AND COMPUTED?
Who can interpret this data and how?
Is there any student bias regarding these strategies?
6. LA-based dashboards
(Monitoring and sense-making)
6
S. Charleer, A. V. Moere, J. Klerkx, K. Verbert and T. De Laet, "Learning Analytics Dashboards to Support Adviser-
Student Dialogue," in IEEE Transactions on Learning Technologies, vol. 11, no. 3, pp. 389-399, 1 July-Sept. 2018,
doi: 10.1109/TLT.2017.2720670
How effective is sense-making out of those dashboards?
Do teachers-students need to improve their data literacy?
Can we compensate the sense-making workload?
7. Smart Learning Environments
(Personalized recommendations-resources)
7
S. Serrano-Iglesias, E. Gómez-Sánchez, M. L. Bote-Lorenzo, G. Vega-Gorgojo, A. Ruiz-Calleja and J. I. Asensio-Pérez,
"From Informal to Formal: Connecting Learning Experiences in Smart Learning Environments," 2021 International
Conference on Advanced Learning Technologies (ICALT), 2021, pp. 363-364, doi: 10.1109/ICALT52272.2021.00116.
How is the student model built?
Do teachers/students get involved in the reaction scripts?
What about privacy in informal learning settings?
8. Two dilemmas on Agency (I)
Dilemma 1: Learning Analytics (LA) may be
helpful when embedded in Technology-Enhanced
Learning (TEL) contexts. They are typically
designed by researchers and developers, that best
know about efficiency and effectiveness. But
existing LA solutions mostly ignore teachers as
orchestrators (designers and enactors).
What about teachers’ agency?
8
9. Two dilemmas on Agency (II)
Dilemma 2: Artificial Intelligence (AI) agents that
are using LA may support and eventually maximize
students’ learning but how can they be transparent,
trustful, responsible or ethical?
What about students’ agency?
9
10. What is this talk about
n Discuss the dilemma regarding teachers’
agency when designing and orchestrating LA
solutions
n Analyze models for human-LA complementarity
and teachers’ augmentation
n Formulate design principles for Human-
Centered Learning Analytics (HCLA)
n Illustrate the HCLA approach
10
11. A definition of teachers’ agency
11
Priestley, M., Biesta, G., & Robinson, S. (2015). Teacher agency: What is it and why does it matter? In R. Kneyber & J. Evers (Eds.), Flip
the System: Changing Education from the Ground Up (pp. 134–148). Routledge. https://doi.org/10.4324/9781315678573 (adapted)
Agency entails the capacity of actors to make practical and normative judgments
among alternative possible trajectories of action, in response to the emerging
demands, dilemmas, and ambiguities of presently evolving situations
12. A socio-cultural perspective of
professional agency
12
Eteläpelto, A., Vähäsantanen, K., Hökkä, P., & Paloniemi, S. (2013). What is agency? Conceptualizing professional
agency at work. Educational Research Review, 10, 45–65. https://doi.org/10.1016/j.edurev.2013.05.001 (adapted)
13. Teachers as producers and shapers
13
Jenkins, G. (2020). Teacher agency: the effects of active and passive responses to curriculum change. Australian
Educational Researcher, 47(1), 167–181. https://doi.org/10.1007/s13384-019-00334-2
14. Digital agency
14
Passey, D., Shonfeld, M., Appleby, L., Judge, M., Saito, T., & Smits, A. (2018). Digital Agency: Empowering Equity in
and through Education. Technology, Knowledge and Learning, 23(3), 425–439. https://doi.org/10.1007/s10758-018-
9384-x (adapted)
Control over and adapt to …
Be proactive producers
Be aware of the data
Decide what data is relevant
16. From User-Centered Design to Co-Design
16
User-centred design Co-creation (co-design)
User
Researcher
Designer
Sanders, E. B. N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. Co-design, 4(1), 5-18.
18. Human-Centered Learning Analytics
18
Human centeredness has been identified in other
fields as a characteristic of systems that have been
carefully designed by:
• identifying the critical stakeholders,
• their relationships, and
• the contexts in which those systems will function
.
19. Human-Centered Learning Analytics
19
HCD should involve:
Inclusion via stakeholder participation in the design process
+
Empathic experiences (particularly when making design
decisions).
Giacomin, J. (2014). What is human centred design? The Design Journal,
17(4), 606–623. https://doi.org/10.2752/175630614X140561854801.
20. Human-Centered Learning Analytics
Human-centered design considered harmful…
“Most items in the world have been designed without the
benefit of user studies and the methods of Human-Centered
Design. Yet they do quite well.”
What Adapts? Technology or People?
Don Norman proposes stronger focus on tasks and activities
Norman, D. A. (2005). Human-centered design considered harmful. interactions, 12(4), 14-19.
21. Human-Centered Learning Analytics
the human centered (not centric)
All the human factors,
social factors and
technology factors
interact together under the
human activity umbrella.
22. Augmented teacher
(Human-AI complementarity)
22
Holstein, K., Aleven, V., Rummel, N. (2020). A Conceptual Framework for Human–AI Hybrid Adaptivity in Education.
In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Artificial Intelligence in Education. AIED
2020. Lecture Notes in Computer Science(), vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-
7_20
n Augmentation
– Complementary strengths and weaknesses
– Improvement (co-learning) over time
n Goals
– Optimized objective functions + design
decisions
n Perceptions
– Sense, attention, interpretation
23. Augmented teacher
(Human-AI complementarity)
23
Holstein, K., Aleven, V., Rummel, N. (2020). A Conceptual Framework for Human–AI Hybrid Adaptivity in Education.
In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Artificial Intelligence in Education. AIED
2020. Lecture Notes in Computer Science(), vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-
7_20
n Actions
– Action space, scalability and capacity
n Decisions
– Link perception and action – take effective
pedagogical interventions
n Timing and granularity
– e.g., adaptation by teachers through LA
dashboards, during learn time, regarding a
task
24. Augmented teacher
(Human-centered approach)
24
Holstein, K., & Aleven, V. (2022). Designing for human-AI complementarity in K-12 education. }, ArXiv,
abs/2104.01266
Echevarría, V. Yang, K., Lawrence, L., Rummel, N., Aleven V., (2020). Exploring Human–AI Control Over Dynamic
Transitions Between Individual and Collaborative Learning, In Proceedings of ECTEL 2020
“
n Lumilo project (CMU) on human-AI
partnership in real-world K-12 education
n Co-orchestration (ITS and teachers) of
transitions from individual to group
activities
n Adoption of participatory (human-
centered) approach to design and
development lifecycle
25. Levels of human-centeredness
25
Smuha N.A. (2023). “Pitfalls and pathways for trustworthy Artificial Intelligence in education” in The Ethics of Artificial
Intelligence in Education Practices, Challenges, and Debates, W. Holmes, K. Porayska-Pomsta (Eds). Taylor and
Francis.
n Human in command
– Oversee when and how to use AI/ITS
n Human on the loop
– Participate in design and operation
n Human in the loop
– Get involved in every lifecycle phase
26. Human-Centeredness in MMLA-AIED
26
Kukurova, M. (2022). “Multimodal Learning Analytics in Real-world Practice: A Bridge Too Far?”, Webinar at Spanish
Network of Learning Analytics (SNOLA), May 2022. https://snola.es/2022/05/03/webinar-multimodal-learning-
analytics-in-real-world-practice-a-bridge-too-far-mutlu-cukurova/
27. Some elements to consider
n LA solutions were eventually pushed by new
technological (Data and AI) affordances
n Teachers as designers were not always
considered in complex real-world TEL spaces
n The hybrid AI-human models and their trade-
offs were not fully studied
n Learning theories have not been used
extensively while designing LA solutions
27
28. The complexity of TEL ecosystems
(Hybrid Learning Spaces)
28
Gil, Mor, Dimitriadis & Köppe (2022): Hybrid Learning Spaces, Springer https://doi.org/10.1007/978-3-030-88520-5
29. Design and orchestration
29
Prieto, L. P., Y. Dimitriadis, J. I. Asensio-Pérez, C. K. Looi (2015). “Orchestration in learning technology
research: evaluation of a conceptual framework”. In: Research in Learning Technology 23.0
How to support teachers as designers and reduce/optimize their
orchestration load?
30. Teachers as designers
n Pedagogical knowledge
– Eventually embedded in tools
– Complements / cooperates with the tacit and
explicit knowledge of the teachers
n Teachers
– Are and can serve as designers
– Should participate in the design and
orchestration of the teaching and learning
processes
30
Kali, McKenney & Sagy (2015)
32. Balancing computer-human agents
32
Sharples, M. (2013). Shared Orchestration Within and Beyond the Classroom. Computers &
Education. 69. 504-506. 10.1016/j.compedu.2013.04.014.
33. Mirroring, Advising, Guiding through LA
33
Soller, A., Martínez-Monés, A., Jermann, P., Muehlenbrock, M. (2005) From Mirroring to Guiding:
A Review of the State of the Art Technology for Supporting Collaborative Learning International
Journal of Artificial Intelligence in Education (ijAIED). 15:261-290
34. Distributed scaffolding
34
• Puntambekar, S. Distributed Scaffolding: Scaffolding Students in Classroom Environments.
Educ Psychol Rev (2021). https://doi.org/10.1007/s10648-021-09636-3
• https://www.imec-int.com/en/research-portfolio/steams: Supporting TEAMS in ambient learning
spaces
Across
1. Tools and social scaffolds
2. Levels (individual, group, and whole class)
3. Time and Contexts
35. A Hybrid human-AI learning model
35
• Molenaar, I. (2021), "Personalisation of learning: Towards hybrid human-AI learning
technologies", in OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial
Intelligence, Blockchain and Robots, OECD Publishing, Paris,
https://doi.org/10.1787/2cc25e37-en.
36. Human-AI extended model
n Teacher monitors and controls
– the learning design prior to execution
(configuration phase)
– the orchestration of the lesson (runtime)
n Learner monitors and controls learning
– Orientation and planning prior to execution
– Monitoring and control during execution
– Reflection after execution
36
37. Human-AI extended model
Timing and phases
Detect (data)
Diagnose (technique/algorithm)
Act (action)
Act components
– LA Perspective
n Inform, Advise, Guide, Recommend
– ITS Perspective
n Step, Task, Curriculum
37
38. Human-AI extended model
n The transitions of control and monitoring have
profound implications for the professional
functioning (agency) of teachers
– Giving up task has positive sides (less time on
correction, more feedback)
– but also, negative sides (less insights and
control).
n This friction cannot be resolved easily but co-
creation processes do allow for a careful
articulation of this friction
38
39. Human-AI extended model
n Static or dynamic balance
– redesign and reconfiguration
– self-, co-, socially shared regulation
n Operators for teachers’ augmentation
– Transparency, agency, explainability, …
39
40. Hybrid Intelligence
40
D. Dellermann, P. Ebel, M. Soellner, J.M. Lerimesiter, “Hybrid Intelligence”, arXiv:2105.00691v1
[cs.AI]
“… the most likely paradigm for the division of labor between
humans and machines in the next years, or probably decades,
is hybrid intelligence. … to try to combine the complementary
strengths of heterogeneous intelligences (i.e., human and
artificial agents) into a socio-technological ensemble. We
envision hybrid intelligence systems, … to accomplish complex
goals by combining human and artificial intelligence to
collectively achieve superior results than each of the could have
done in separation and continuously improve by learning from
each other”
41. Hybrid Intelligence
41
Z. Akata et al., (2020) "A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect
With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence," Computer,
53(8), 18-28, doi: 10.1109/MC.2020.2996587
n “… Hybrid intelligence (HI) can go well beyond this by creating systems
that operate as mixed teams, where humans and machines cooperate
synergistically, proactively, and purposefully to achieve shared goals,
showing AI’s potential for amplifying instead of replacing human
intelligence”
n “Collaborative HI: How do we develop AI systems that work in
synergy with humans?
› Adaptive HI: How can these systems learn from and adapt to humans
and their environment?
› Responsible HI: How do we ensure that they behave ethically and
responsibly?
› Explainable HI: How can AI systems and humans share and explain
their awareness, goals, and strategies?”
42. AI and the future of learning
42
Roschelle, J., Lester, J. & Fusco, J. (Eds.) (2020). AI and the future of learning: Expert panel
Report. Digital Promise. https://circls.org/reports/ai-report.
1. Investigate AI Designs for an Expanded Range of Learning Scenarios
2. Develop AI Systems that Assist Teachers and Improve Teaching
3. Intensify and Expand Research on AI for Assessment of Learning
4. Accelerate Development of Human-Centered or Responsible AI
5. Develop Stronger Policies for Ethics and Equity
6. Inform and Involve Educational Policy Makers and Practitioners.
7. Strengthen the Overall AI and Education Ecosystem
Seven recommendations from US expert panel
43. Human-centered and trustworthy AI
43
• Delgado Kloos, C., et al. (2022), H2O Learn - Hybrid and Human-Oriented Learning: Trustworthy and Human-
Centered Learning Analytics (TaHCLA) for Hybrid Education. IEEE Global Engineering Education
Conference, EDUCON 2022,
• HLEG-AI (High-Level Expert Group on Artificial Intelligence) (2019), “Ethics Guidelines for Trustworthy AI:
Requirements of Trustworthy AI,” Available: https://ec.europa.eu/futurium/en/ai-alliance-consultation/guidelines/1
44. Human-Centered Approaches ...
See also “sister” initiatives for Human-Centered
approaches for the design and development of truly
mixed human-AI initiatives for human empowerment
e.g., Recent EU call for funding of A HUMAN-
CENTRED AND ETHICAL DEVELOPMENT OF
DIGITAL AND INDUSTRIAL TECHNOLOGIES 2022
(HORIZON-CL4-2022-HUMAN-02)
44
45. And a few suggestions …
n Bring together LA and Learning Design (LD)
n Consider multiple needs and paths to use LA,
implemented as adaptive (by system/agent) or
adaptable (by users)
n Bring the teacher in the loop and orchestrate LA
with all stakeholders (OrLA)
n Consider the consolidated model for LA
n Adopt human-oriented workflows for LA solutions
n Consider data storytelling and explanatory LA
45
46. LD-based process for LA solutions
46
1 – LA design: LD elements selected as targets for LA solution
2 – LA implementation:
2a. Data from LA targets is analyzed by the LA tool
Resulting LA informs: 2b.) orchestration, 2c.) assessment
Dimitriadis, Martínez-Maldonado & Wiley (2020)
47. Consolidated model for LA
47
• Gasevic, Dawson & Siemens (2015)
• Saint, Gasevic, Matcha, Ahmad & Pardo (2020)
• Gasevic, Kovanovic & Joksimovic (2017) - figure
• Reimann (2016)
49. LATUX workflow for LA solutions
49
• Martinez-Maldonado, Pardo, Mirriahi, Yacef, Kay & Clayphan (2016) - figure
• Holstein, McLaren & Aleven (2019)
50. Datastorytelling and explanatory LA
50
Echeverria, Martinez-Maldonado, Buckingham Shum, Chiluiza, Granda & Conati (2018) - figures
51. HCD principles for actionable LA
solutions
1. Agentic positioning of teachers and other
stakeholders
2. Integration of the learning design cycle and the LA
design process
3. Reliance on educational theories to guide the LA
solution design and implementation
51
Y.Dimitriadis, K.Wiley, & R.Martínez-Maldonado (2021)
52. Illustrative study
52
From Theory to Action:
Developing and Evaluating Learning
Analytics for Learning Design
• K. Wiley, Y. Dimitriadis, A. Bradford, & M. Linn (2020)
• K. Wiley (2020)
• Y. Dimitriadis, K. Wiley, & R. Martínez-Maldonado (2021)
53. An overview of the study
n Design and development of Teacher Action Planner,
a LA tool that supports teachers’ orchestration
actions:
– Grounded on learning theory (Knowledge Integration)
and using the Inquiry Based Learning approach.
– Aligned with the Learning Design (Global Climate
Change and Photosynthesis Units) and platform (WISE)
– Aligned with stakeholders’ needs (OrLA)
– Functional within the constraints of the technical and
learning environments
53
66. Human-Centered Design of LA
n Eventually the benefits of enhanced agency,
adoption and impact of the LA solutions
overcome the costs of difficult, time and
resource consuming participatory processes
n All the important aspects of learning
(cognitive, metacognitive, affective and social)
are highly sensible and dependent on the
context
66
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-Centred Learning
Analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1
67. Some take-home messages (I)
n Technology-enhanced learning (TEL) ecosystems
– Especially hard to design and orchestrate (advising)
n Teachers are essential stakeholders
– LD and LA are both about learning and teaching
n Human-Centered design is necessary despite its cost
– Move from “demonstrators in a greenfield” to embedded tools and
practices in authentic contexts
n Tools are necessary to support stakeholders
– Balanced use of AI agents and human expertise and actions through
orchestration technology and distributed scaffolding
n Keep the power of LA-based models
– But complement with explanations, trust, privacy
67
68. Some take-home messages (II)
n LA for understanding and optimizing learning
– oriented to pedagogical interventions based on actionable insights
n LA benefits from
– Data Science, Learning Theory and Design
n LA and LD are intrinsically interconnected
– They should be jointly employed
n Inter-stakeholder communication is essential
– using multiple design techniques and approaches
n Bring the human in the loop
– Through participatory user-centered design processes
n Support teachers (and learners) with
– technological and conceptual tools
68
69. Some HCLA challenges
● Can design processes from other disciplines, such as HCI,
Co-Design and Participatory design, be unproblematically
adopted for HCLA, or do they require adaptation?
● What are the obstacles to the adoption of HCLA design
processes?
● How can the voice of students be taken more into account,
besides the dominant thread of involving teachers?
● What are the lessons learnt from mid-to-long term HCLA
studies and how do they inform the aforementioned topic of
adoption?
● HCLA beyond conventional higher education
● A wider view of human-centeredness
● Human-AI complementarity
69
71. Let’s remember:
Learning Analytics are about
… Learning
… Learners
…Teachers
… Humans
… Society
This is why
Human-Centered Learning Analytics
may be worth considering