Keynote at HELMeTO2022 conference, Palermo, Italy on recent research in Search As Learning (SAL), at the intersection of machine learning and cognitive psychology.
Analysing User Knowledge, Competence and Learning during Online ActivitiesStefan Dietze
Research talk given at Italian National Research Council (CNR), Institute for Educational Technologies (ITD) on learning analytics in everyday online activities.
Developing a multiple-document-processing performance assessment for epistem...Simon Knight
http://oro.open.ac.uk/41711/
The LAK15 theme “shifts the focus from data to impact”, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory.
Analysing User Knowledge, Competence and Learning during Online ActivitiesStefan Dietze
Research talk given at Italian National Research Council (CNR), Institute for Educational Technologies (ITD) on learning analytics in everyday online activities.
Developing a multiple-document-processing performance assessment for epistem...Simon Knight
http://oro.open.ac.uk/41711/
The LAK15 theme “shifts the focus from data to impact”, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory.
Confronting Reality with Big Data & Learning Analytics
We are experiencing an explosion in the quantity of data available online from archives and live streams. Learning Analytics is concerned with how educational research, and learning platform design, can make more effective use of such data (Long & Siemens, 2011). Improving outcomes through the analysis of data is of interest to researchers, administrators, systems architects, social media developers, educators and learners. Analytics are being held up by some as a way to confront, and tackle, the tough new realities of less money, less attention, and higher accountability for quality of learning.
Researchers and vendors are building reporting capabilities into tools that provide unprecedented levels of data on learners. This symposium will show what is possible, and what's coming soon. What objections could possibly be raised to such progress?
However, information infrastructure embodies and shapes worldviews: classification schemes are not only systematic ways to capture and preserve, but also to forget, by virtue of what remains invisible (Bowker & Star, 1999). Learning analytics and recommendation engines are designed with a particular conception of ‘success’, driving the patterns deemed to be evidence of progress, the interventions that are deemed appropriate, the data captured and the rules that fire in software.
This symposium will air some of the critical arguments around the limits of decontextualised data and automated analytics, which often appear reductionist in nature, failing to illuminate higher order learning. There are complex ethical issues around data fusion, and it is not clear to what extent learners are empowered, in contrast to being merely the objects of tracking technology. Educators may also find themselves at the receiving end of a new battery of institutional ‘performance indicators’ that do not reflect what they consider to be authentic learning and teaching.
This Symposium will provide the opportunity to hear a series of brief presentations introducing contrasting perspectives, before the debate is opened to all. Speakers from a cross-section of The Open University will describe how we are connecting datasets, analysing student data and prototyping next generation analytics. Complementing this, JISC will present a national capability perspective, with an update on the JISC CETIS ‘landscape analysis’ of the field, which will clarify potential benefits, issues to consider, and help institutions to assess their current capability and possible next steps.
Participants will catch up with developments in this fast moving field, through exposure to the possibilities of analytics, as well as issues to be alert to.
Semantic Technologies in Learning EnvironmentsDragan Gasevic
Invited talk delivered in the scope of an open online course: Introduction to Learning and Knowledge Analytics
Details about the course, and the recorded presentation can be found at
http://www.learninganalytics.net/?page_id=71
This slides are for a presentation at the 2009 IEEE/WIC/ACM International Conference on Web Intelligence. The major emphasis to this paper is concentrating on how to provide more personalized search support for a specific user considering his/her historical interests or recent interests. Cognitive memory retention like models are proposed and implemented in this system. Other supporting functionalities, such as domain distribution support, etc. are briefly mentioned. The whole paper can be downloaded from http://www.iwici.org/~yizeng/papers/WI2009-camera-ready.pdf
Learning Analytics -Towards a New Discipline-Dragan Gasevic
The talk, motivated by the present state of learning and education, identifies a need for a systematic change of the present preactice. Learning analytics is identified as a possible way to good to address this open challenge. Some connections with evidence-based medicine are drawn. Finally, learning analytics is defined as well as some open research challenges.
ALA 2015 Invited Research Talk: Youth Collaborative Information Practices Dur...Rebecca Reynolds
This presentation was delivered as part of an ALA Conference 2015 special research session, "Out of the Library School and Into the School Library," sponsored by the Institute for Museum and Library Services. The session featured presentations of research findings stemming from the work of recent Early Career Development Grant awardees.
Keynote Address, Expanding Horizons 2012, Macquarie University
http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons
"Learning Analytics": unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. "Benchmarking" may trigger dystopic visions of dumbed down proxies for 'real teaching and learning', but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.
A Complete Analysis of Human Action Recognition Proceduresijtsrd
Due to concerns like backdrop cluttering, incomplete obstruction, scale disparities, viewpoint, illumination, and appearance, identifying activities of humans from a sequence of video or still photos are a complex issue. Multiple movement recognition structures is necessary for numerous applications, such as a video investigation mechanism, human computer interface HCI , and robotics for characterising human behaviour. In this work, we bestow a comprehensive assessment of recent and advanced designs involved in the classification of human activity. We outline a classification of human activity approaches and go through their benefits and drawbacks. Specifically, we classify human activities categorization approaches into two broad classes based on if or not they make use of information from several modes. Next, each of these classes is broken down into its subclasses, which illustrate how each category models human activity. Monisa Nazir | Shalini Bhadola | Kirti Bhaia | Rohini Sharma "A Complete Analysis of Human Action Recognition Procedures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50522.pdf Paper URL: https://www.ijtsrd.com/computer-science/cognitive-science/50522/a-complete-analysis-of-human-action-recognition-procedures/monisa-nazir
JISC RSC London Workshop - Learner analyticsJames Ballard
Introduction to learning analytics and approaches to learner engagement to raise awareness and set the seen for upcoming projects and advice for supported learning providers.
Mining and Understanding Activities and Resources on the WebStefan Dietze
Research Seminar at KMRC Tübingen, Germany, on mining and understanding of Web acivities and resources through knowledge discovery and machine learning approaches.
Analytic and strategic challenges of serious gamesDavid Gibson
How higher education learning and teaching can learn from serious game developers. Keynote at the 5th annual SeGAH conference concurrent with WWW 2017 held in Perth, Western Australia
ADL’s recent research review uncovered the fact that very few actual ID models for mobile learning truly exist. Instead of creating a new ID model, they have presented a framework that can be used to incorporate mobile learning considerations into existing ID models and agile approaches to optimize them for the mobile learner. Ideally, instructional designers should now consider focusing on new opportunities for improving performance and augmenting skills, not just on knowledge transfer.
The flexible approach proposed by the framework takes both instruction and performance support into consideration for the mobile learning task or challenge at hand. This session will provide you with ADL’s mobile learning research findings and an overview of the MoTIF project. This session will specifically address the mLearning considerations during the analysis and design phases. Participants will also receive a list of mobile learning resources and discuss opportunities for getting involved with the community supporting this effort and evolving the framework.
What Can IA Learn from LIS? Perspectives from LIS Educationcraigmmacdonald
Morville & Rosenfeld's "Information Architecture for the World Wide Web" positioned IA as an approach to web/interface design that is deeply embedded in, and strongly informed by, the LIS discipline. To re-consider of the impact of the LIS discipline on the IA profession, this presentation (and a subsequent paper) reports the preliminary results of an analysis of syllabi of information architecture courses offered by graduate schools of Library and Information Science in the United States and Canada.
Presented for the Teaching IA workshop at the 2014 IA Summit in San Diego, CA.
Understanding Scientific and Societal Adoption and Impact of Science Through ...Stefan Dietze
Keynote on analysing scholarly discourse at Second International Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data SemTech4STLD, held on 26 May at ESWC2024
Confronting Reality with Big Data & Learning Analytics
We are experiencing an explosion in the quantity of data available online from archives and live streams. Learning Analytics is concerned with how educational research, and learning platform design, can make more effective use of such data (Long & Siemens, 2011). Improving outcomes through the analysis of data is of interest to researchers, administrators, systems architects, social media developers, educators and learners. Analytics are being held up by some as a way to confront, and tackle, the tough new realities of less money, less attention, and higher accountability for quality of learning.
Researchers and vendors are building reporting capabilities into tools that provide unprecedented levels of data on learners. This symposium will show what is possible, and what's coming soon. What objections could possibly be raised to such progress?
However, information infrastructure embodies and shapes worldviews: classification schemes are not only systematic ways to capture and preserve, but also to forget, by virtue of what remains invisible (Bowker & Star, 1999). Learning analytics and recommendation engines are designed with a particular conception of ‘success’, driving the patterns deemed to be evidence of progress, the interventions that are deemed appropriate, the data captured and the rules that fire in software.
This symposium will air some of the critical arguments around the limits of decontextualised data and automated analytics, which often appear reductionist in nature, failing to illuminate higher order learning. There are complex ethical issues around data fusion, and it is not clear to what extent learners are empowered, in contrast to being merely the objects of tracking technology. Educators may also find themselves at the receiving end of a new battery of institutional ‘performance indicators’ that do not reflect what they consider to be authentic learning and teaching.
This Symposium will provide the opportunity to hear a series of brief presentations introducing contrasting perspectives, before the debate is opened to all. Speakers from a cross-section of The Open University will describe how we are connecting datasets, analysing student data and prototyping next generation analytics. Complementing this, JISC will present a national capability perspective, with an update on the JISC CETIS ‘landscape analysis’ of the field, which will clarify potential benefits, issues to consider, and help institutions to assess their current capability and possible next steps.
Participants will catch up with developments in this fast moving field, through exposure to the possibilities of analytics, as well as issues to be alert to.
Semantic Technologies in Learning EnvironmentsDragan Gasevic
Invited talk delivered in the scope of an open online course: Introduction to Learning and Knowledge Analytics
Details about the course, and the recorded presentation can be found at
http://www.learninganalytics.net/?page_id=71
This slides are for a presentation at the 2009 IEEE/WIC/ACM International Conference on Web Intelligence. The major emphasis to this paper is concentrating on how to provide more personalized search support for a specific user considering his/her historical interests or recent interests. Cognitive memory retention like models are proposed and implemented in this system. Other supporting functionalities, such as domain distribution support, etc. are briefly mentioned. The whole paper can be downloaded from http://www.iwici.org/~yizeng/papers/WI2009-camera-ready.pdf
Learning Analytics -Towards a New Discipline-Dragan Gasevic
The talk, motivated by the present state of learning and education, identifies a need for a systematic change of the present preactice. Learning analytics is identified as a possible way to good to address this open challenge. Some connections with evidence-based medicine are drawn. Finally, learning analytics is defined as well as some open research challenges.
ALA 2015 Invited Research Talk: Youth Collaborative Information Practices Dur...Rebecca Reynolds
This presentation was delivered as part of an ALA Conference 2015 special research session, "Out of the Library School and Into the School Library," sponsored by the Institute for Museum and Library Services. The session featured presentations of research findings stemming from the work of recent Early Career Development Grant awardees.
Keynote Address, Expanding Horizons 2012, Macquarie University
http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons
"Learning Analytics": unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. "Benchmarking" may trigger dystopic visions of dumbed down proxies for 'real teaching and learning', but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.
A Complete Analysis of Human Action Recognition Proceduresijtsrd
Due to concerns like backdrop cluttering, incomplete obstruction, scale disparities, viewpoint, illumination, and appearance, identifying activities of humans from a sequence of video or still photos are a complex issue. Multiple movement recognition structures is necessary for numerous applications, such as a video investigation mechanism, human computer interface HCI , and robotics for characterising human behaviour. In this work, we bestow a comprehensive assessment of recent and advanced designs involved in the classification of human activity. We outline a classification of human activity approaches and go through their benefits and drawbacks. Specifically, we classify human activities categorization approaches into two broad classes based on if or not they make use of information from several modes. Next, each of these classes is broken down into its subclasses, which illustrate how each category models human activity. Monisa Nazir | Shalini Bhadola | Kirti Bhaia | Rohini Sharma "A Complete Analysis of Human Action Recognition Procedures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50522.pdf Paper URL: https://www.ijtsrd.com/computer-science/cognitive-science/50522/a-complete-analysis-of-human-action-recognition-procedures/monisa-nazir
JISC RSC London Workshop - Learner analyticsJames Ballard
Introduction to learning analytics and approaches to learner engagement to raise awareness and set the seen for upcoming projects and advice for supported learning providers.
Mining and Understanding Activities and Resources on the WebStefan Dietze
Research Seminar at KMRC Tübingen, Germany, on mining and understanding of Web acivities and resources through knowledge discovery and machine learning approaches.
Analytic and strategic challenges of serious gamesDavid Gibson
How higher education learning and teaching can learn from serious game developers. Keynote at the 5th annual SeGAH conference concurrent with WWW 2017 held in Perth, Western Australia
ADL’s recent research review uncovered the fact that very few actual ID models for mobile learning truly exist. Instead of creating a new ID model, they have presented a framework that can be used to incorporate mobile learning considerations into existing ID models and agile approaches to optimize them for the mobile learner. Ideally, instructional designers should now consider focusing on new opportunities for improving performance and augmenting skills, not just on knowledge transfer.
The flexible approach proposed by the framework takes both instruction and performance support into consideration for the mobile learning task or challenge at hand. This session will provide you with ADL’s mobile learning research findings and an overview of the MoTIF project. This session will specifically address the mLearning considerations during the analysis and design phases. Participants will also receive a list of mobile learning resources and discuss opportunities for getting involved with the community supporting this effort and evolving the framework.
What Can IA Learn from LIS? Perspectives from LIS Educationcraigmmacdonald
Morville & Rosenfeld's "Information Architecture for the World Wide Web" positioned IA as an approach to web/interface design that is deeply embedded in, and strongly informed by, the LIS discipline. To re-consider of the impact of the LIS discipline on the IA profession, this presentation (and a subsequent paper) reports the preliminary results of an analysis of syllabi of information architecture courses offered by graduate schools of Library and Information Science in the United States and Canada.
Presented for the Teaching IA workshop at the 2014 IA Summit in San Diego, CA.
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An interdisciplinary journey with the SAL spaceship – results and challenges in the emerging field of Search As Learning (SAL)
1. An interdisciplinary journey with the SAL spaceship –
results and challenges in the emerging field of Search
As Learning (SAL)
HELMETO Conference, Palermo
Stefan Dietze, 22.09.2022
2. Informal and microlearning on the Web
2
▪ Anything can be a learning resource
▪ The activity makes the difference (not only
the resource): i.e. how a resource is being
used
▪ Learning Analytics data in online/non-
learning environments?
o Activity streams,
o Social graphs (and their evolution),
o Behavioural traces (mouse movements,
keystrokes)
o ...
▪ Research challenges:
o How to detect „learning“?
o How to detect learning-specific notions
such as „competences“, „learning
performance“ etc?
3. SAL = „Search As Learning“
3
Research challenges at the intersection of AI/ML,
HCI & cognitive psychology
▪ Detecting coherent search missions?
▪ Detecting learning throughout search?
detecting “informational” search missions (as
opposed to “transactional” or “navigational”
missions)
▪ How competent is the user? –
Predict/understand knowledge state of users
based on in-session behavior/interactions
▪ How well does a user achieve his/her learning
goal/information need? - Predict knowledge gain
throughout search session
Hoppe, A., Holtz, P., Kammerer, Y., Yu, R., Dietze, S., Ewerth, R., Current Challenges for Studying Search as Learning Processes, 7th Workshop on
Learning & Education with Web Data (LILE2018), in conjunction with ACM Web Science 2018 (WebSci18), Amsterdam, NL, 27 May, 2018.
5. „SAL Spaceship“ – an interdisciplinary, conceptual SAL framework
5
Von Hoyer, J., Hoppe, A., Kammerer, Y., Otto, C., Pardi, G., Rokicki, M., Yu, R., Dietze, S., Ewerth, R., Holtz, P., The SAL Spaceship: Towards a comprehensive model
of psychological and technological facets of search as learning (SAL), Frontiers in Psychology, Section Human-Media Interaction, 2022.
6. SAL Spaceship
Focus: Learner
6
• Model focuses on
„informational“ search
• Cf. search intent
taxonomy by Broder,
2002
• Parts of it applicable to
other types of search
intents („transactional“,
„navigational“)
7. Can we detect „learning“ in Web Search?
Yu, R., Limock, Dietze, S., Still Haven’t Found What You’re Looking For - Detecting the Intent of Web Search Missions from User Interaction Features. CoRR abs/2207.01256
▪ Segmentation of real-world query logs (AOL dataset)
into logical and physical sessions
▪ Segments from Hagen et al. 2013 (2881 logical
sessions, 1378 missions, average 6.5 queries per
session)
▪ Task: supervised classification of mission intent into
transactional, navigational and informational
▪ Basic machine learning models (DT, RF, LR, SVM)
▪ 22 features in 3 categories (query, browsing,
mission)
8. Can we detect „learning“ in Web Search?
Yu, R., Limock, Dietze, S., Still Haven’t Found What You’re Looking For - Detecting the Intent of Web Search Missions from User Interaction Features. CoRR abs/2207.01256
9. Can we detect „learning“ in Web Search?
Yu, R., Limock, Dietze, S., Still Haven’t Found What You’re Looking For - Detecting the Intent of Web Search Missions from User Interaction Features. CoRR abs/2207.01256
10. Understanding knowledge gain/state of users during search
Gadiraju, U., Yu, R., Dietze, S., Holtz, P.,. Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM CHIIR 2018.
Data collection - summary
▪ Crowdsourced collection of search session data
▪ 10 search topics (e.g. “Altitude sickness”,
“Tornados”), incl. pre- and post-tests
▪ Approx. 1000 distinct crowd workers & 100
sessions per topic
▪ Tracking of user behavior through 76 features
in 5 categories (session, query, SERP – search
engine result page, browsing, mouse traces)
11. Understanding knowledge gain/state of users during search
11
Some results
▪ 70% of users exhibited a knowledge gain (KG)
▪ Negative relationship between KG of users and
topic popularity (avg. accuracy of workers in
knowledge tests) (R= -.87)
▪ Amount of time users actively spent on web pages
describes 7% of the variance in their KG
▪ Query complexity explains 25% of the variance in
the KG of users
▪ Topic-dependent behavior: search behavior
correlates stronger with search topic than with
KG/KS
Gadiraju, U., Yu, R., Dietze, S., Holtz, P.,. Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM CHIIR 2018.
13. ▪ Same session data as Gadiraju et al., 2018
▪ Stratification of users into classes: user knowledge state (KS)
and knowledge gain (KG) into {low, moderate, high} using
(low < (mean ± 0.5 SD) < high)
▪ Supervised multiclass classification
(Naive Bayes, Logistic regression, SVM, random forest, multilayer perceptron)
▪ KG prediction performance results (after 10-fold cross-validation)
▪ Considers in-session features (behavioural traces) only
Predicting knowledge gain/state during web search
13
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., Dietze, S., Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM SIGIR 2018.
14. Predicting knowledge gain/state during SAL: Features
14
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., Dietze, S., Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM SIGIR 2018.
Behavioral
features
15. ▪ Feature importance (knowledge gain prediction task)
Predicting knowledge gain/state during web search
15
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., Dietze, S., Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM SIGIR 2018.
16. ▪ Feature importance (knowledge state prediction task)
Predicting knowledge gain/state during web search
16
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., Dietze, S., Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM SIGIR 2018.
17. Does topic familiarity influence search/learning behaviour?
17
Davari, M., Yu., R., Dietze, S., Understanding the Influence of Topic Familiarity on Search Behavior in Digital Libraries, EARS 2019 – International Workshop
on ExplainAble Recommendation and Search, collocated with SIGIR2019, Paris, July 2019.
▪ Small lab study (N=25) using eyetracking data in
digital library / scholarly literature search (SowiPort)
▪ 50 sessions (for each user one on familiar topic, one
on unfamiliar topic)
▪ 2344 web pages viewed (SERPs, actual docs); 2.6 M
rows of eye tracking data
▪ Familiar tasks: more fixated terms, longer sessions
and less query term variance (indicators for prior
competence)
▪ Unfamiliar tasks: more focus on SERPs (rather than
actual resources)
▪ Yet in unfamiliar tasks, only 51.2% of query terms
are fixated before acquisition (compared to 60.7%
for familiar tasks)
20. Understanding the impact of (learning) resource characteristics
20
Yu, R., Tang, R., Rokicki, M., Gadiraju, U., Dietze, S., Topic-independent Modeling of User Knowledge in Informational Search Sessions.
Information Retrieval Journal (2021): 1-29
▪ Understanding the relation between Web resource features (e.g. resource complexity, language, length)
and a user’s knowledge state (KS) and knowledge gain (KG).
▪ Understanding the topic-specificity of individual features, i.e. dependency between feature performance
and information needs (topics)
▪ Building generalizable ML models that can be used in real-world search environments on unseen topics for
predicting learning & competence from both behavioral and resource-centric features
▪ Approach/experimental setup: same dataset from SIGIR2018, but additional features and feature selection
strategy (maximise correlation with target variable and generalizability)
21. Yu, R., Tang, R., Rokicki, M., Gadiraju, U., Dietze, S., Topic-independent Modeling of User Knowledge in Informational Search Sessions.
Information Retrieval Journal (2021): 1-29
Characteristics of (learning) resources (instead of behaviour)
21
Web resource features
& correlation coefficients
(highlighted: p > 0.05)
22. Characteristics of (learning) resources (instead of behaviour)
22
▪ Model performance on knowledge state prediction & knowledge gain prediction
▪ Significant improvements across all classes
KS
New: this work (IRJ21)
Baseline: SIGIR2018 (previous slides)
Yu, R., Tang, R., Rokicki, M., Gadiraju, U., Dietze, S., Topic-independent Modeling of User Knowledge in Informational Search Sessions.
Information Retrieval Journal (2021): 1-29
23. How does multimodality affect the knowledge (g/s) prediction?
23
Otto, C., Yu, R., Pardi, G., von Hoyer, J., Rokicki, M., Hoppe, A., Holtz, P., Kammerer, Y., Dietze, S., Ewerth, E., Predicting Knowledge Gain during Web Search
based on Multimedia Resource Consumption, 22nd International Conference on Artificial Intelligence in Education (AIED2021), Springer, 2021.
▪ Lab study for data collection (N=113)
▪ Topic: “Lightning & thunderstorms” (causal chain of events, including
declarative and procedural knowledge)
▪ Knowledge test: 10 item multiple choice test pre-/post
▪ Tracking of behavioral features & text features
(all 110 features from IRJ)
▪ Additionally: multimedia features & eye tracking
o Detect learning frames (actual reading) in screencast (as opposed to
navigation/procrastination)
o Detecting key structure (heading, menu, content list, text, images …)
o Classifying image types (Infographics, Indoor Photo, Map, Outdoor
Photo, Technical Drawing, Information Visualization)
Classifier trained through weak labels (images crawled through
Google Image Search)
24. How does multimodality affect the knowledge (g/s) prediction?
24
Otto, C., Yu, R., Pardi, G., von Hoyer, J., Rokicki, M., Hoppe, A., Holtz, P., Kammerer, Y., Dietze, S., Ewerth, E., Predicting Knowledge Gain during Web Search
based on Multimedia Resource Consumption, 22nd International Conference on Artificial Intelligence in Education (AIED2021), Springer, 2021.
▪ Results for knowledge gain prediction (TI = text features, MI = multimedia features)
▪ Feature importance (Mean Decrease in Impurity) in RF model
IRJ2021
25. Facilitating SAL research through public research data
25
https://data.uni-hannover.de/dataset/sal-dataset
Otto, C., Rokicki, M., Pardi, G., Gritz, W., Hienert, D.,Yu, R., Hoyer, J., Hoppe, A., Dietze, S., Holtz, P., Kammerer, Y., Ewerth, R., SaL-Lightning Dataset: Search and Eye
Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search, ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR2022).
27. Learning on the Web beyond Google et al.
E.g.: Are Twitter users not learning too?
27
https://ai4sci-project.org/
Science claim
Science reference
Science relevance
No science
Science reference
Hafid, S., Schellhammer, S., Bringay, S., Todorov, K., Dietze, S., "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online
Discourse", CIKM2022
28. Learning on the Web beyond Google et al.
Science discourse is on the rise
28
▪ AI4Sci project: understanding and classification of science discourse online (news, social Web)
https://ai4sci-project.org/
▪ Percentage of tweets containing
links to scientific articles (journals,
publishers, science blogs etc)
▪ Uses list of > 30 K science web
domains
▪ Data source: TweetsKB
(https://data.gesis.org/tweetskb/),
> 10 bn tweets archived since 2013
29. Learning on the Web beyond Google et al.
Science discourse is on the rise
29
https://ai4sci-project.org/
SciBERT classifier
Heuristic: Sci term
Sci subdomain
30. SciTweets dataset & classifier
30
▪ Ground truth dataset, heuristics-based sampling
strategy and annotation framework for testing
classification models
▪ 1261 expert-labeled tweets across all
classes/labels
▪ Baseline classifiers based on SciBERT transformer
model (fine-tuned/tested on SciTweets)
▪ Ongoing: analysis of large-scale science discourse
and its evolution
https://ai4sci-project.org/
Hafid, S., Schellhammer, S., Bringay, S., Todorov, K., Dietze, S., SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse,
CIKM2022
31. The SAL Spaceship in the context of ubiquitous online learning
31
Von Hoyer, J., Hoppe, A., Kammerer, Y., Otto, C., Pardi, G., Rokicki, M., Yu, R., Dietze, S., Ewerth, R., Holtz, P., The SAL Spaceship: Towards a comprehensive
model of psychological and technological facets of search as learning (SAL), Frontiers in Psychology, Section Human-Media Interaction, 2022.
32. ▪ Knowledge acquisition (learning) is a ubiquitous activity on the Web
▪ Search as learning = specific case of informal/microlearning during Web search & browsing
▪ Behavioural traces (e.g. scrolling, queries, browsing, mouse traces etc) are crucial indicators to
distinct learning from other activities
▪ Behavioural traces also facilitate user modeling/classification: prediction of knowledge state
(competence) and gain (learning) without any prior knowledge of the user
▪ Resource features (e.g. complexity, language) improve classification significantly
▪ Multimodal features likely to provide useful indicators
▪ Learning is ubiquitous also in social platforms (science discourse as specific case)
▪ Data is very costly (lab studies, crowdsourced session data)
Key take-aways
32
33. References
33
• Hoppe, A., Holtz, P., Kammerer, Y., Yu, R., Dietze, S., Ewerth, R., LILE2018, in conjunction with ACM Web Science 2018 (WebSci18), Amsterdam,
NL, 27 May, 2018.
• Von Hoyer, J., Hoppe, A., Kammerer, Y., Otto, C., Pardi, G., Rokicki, M., Yu, R., Dietze, S., Ewerth, R., Holtz, P., The SAL Spaceship: Towards a
comprehensive model of psychological and technological facets of search as learning (SAL), Frontiers in Psychology, Section Human-Media
Interaction, 2022.
• Yu, R., Limock, Dietze, S., Still Haven’t Found What You’re Looking For - Detecting the Intent of Web Search Missions from User Interaction
Features. CoRR abs/2207.01256
• Gadiraju, U., Yu, R., Dietze, S., Holtz, P.,. Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web. ACM CHIIR 2018.
• Davari, M., Yu., R., Dietze, S., Understanding the Influence of Topic Familiarity on Search Behavior in Digital Libraries, EARS 2019 – International
Workshop on ExplainAble Recommendation and Search, collocated with SIGIR2019, Paris, July 2019.
• Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., Dietze, S., Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web.
ACM SIGIR 2018.
• Yu, R., Tang, R., Rokicki, M., Gadiraju, U., Dietze, S., Topic-independent Modeling of User Knowledge in Informational Search Sessions.
Information Retrieval Journal (2021): 1-29
• Otto, C., Yu, R., Pardi, G., von Hoyer, J., Rokicki, M., Hoppe, A., Holtz, P., Kammerer, Y., Dietze, S., Ewerth, E., Predicting Knowledge Gain during
Web Search based on Multimedia Resource Consumption, 22nd International Conference on Artificial Intelligence in Education (AIED2021),
Springer, 2021.
• Otto, C., Rokicki, M., Pardi, G., Gritz, W., Hienert, D.,Yu, R., Hoyer, J., Hoppe, A., Dietze, S., Holtz, P., Kammerer, Y., Ewerth, R., SaL-Lightning
Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search, ACM CHIIR2022.
• Hafid, S., Schellhammer, S., Bringay, S., Todorov, K., Dietze, S., SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online
Discourse, CIKM2022
34. Acknowledgements & thanks
34
▪ All co-authors
▪ Knowledge Technologies for the Social Sciences @ GESIS
http://gesis.org/en/kts
▪ Data & Knowledge Engineering group at HHU
https://www.cs.hhu.de/en/research-groups/data-knowledge-engineering
▪ SALIENT project team
https://projects.tib.eu/salient/
▪ AI4Sci project team
https://ai4sci-project.org/
▪ Funders: BMBF, Leibniz Association, ANR
▪ The HELMeTO team