Hendrik Drachsler is an assistant professor who researches personalization of learning with recommender systems and educational datasets. His research focuses on using information retrieval technologies like recommender systems to personalize learning. He also researches visualization of educational data and supporting context-awareness through data mining. Drachsler leads a theme team on learning analytics and is involved in several projects applying recommender systems and learning analytics to technology-enhanced learning.
Tips for grabbing and holding attention in online coursesDr Graeme Salter
Just because you put learning material online doesn't mean that students will engage with it (or even view it). This presentation looks at some tips for grabbing and holding attention in online courses.
Autonomics Computing (with some of Adaptive Systems) and Requirements Enginee...Jehn
This presentantion gives an overview on Autonomic Computing. Next, show the state-of-the-art on Requirements Engineering for Autonomic Computing based on 4 papers
projekt, który wysłaliśmy na konkurs pt. Urban legends. publikujemy go w takiej formie dnia 26.03.2009 ze względu na często zdarzające się kradzieże pomysłów i projektów.
Publishing for the students living in the iPad era: our view of the industrySebastien Dubuis
Publishing for the iPad generation of students requires some new mindset. How to enhance textbooks to create a lively reading experience? How to seamlessly offer cross devices compatibility?
What do zen practice and Oracle SQL optimization have in common? You’d be surprised! This presentation demonstrates how the zen principles of simplicity, focus, and practice can be applied to your technical skills to make you a calm and fearless SQL optimizer. You will be “enlightened” as to how to find ease with your day-to-day SQL tuning activities.
Updated version of the RecSys TEL lecture I already gave as invited talk in the UK, NL and DE. The conclusion parts is totally new and aligned to the new book on RecSys for Learning at Springer that will appear soon in 2012.
Tips for grabbing and holding attention in online coursesDr Graeme Salter
Just because you put learning material online doesn't mean that students will engage with it (or even view it). This presentation looks at some tips for grabbing and holding attention in online courses.
Autonomics Computing (with some of Adaptive Systems) and Requirements Enginee...Jehn
This presentantion gives an overview on Autonomic Computing. Next, show the state-of-the-art on Requirements Engineering for Autonomic Computing based on 4 papers
projekt, który wysłaliśmy na konkurs pt. Urban legends. publikujemy go w takiej formie dnia 26.03.2009 ze względu na często zdarzające się kradzieże pomysłów i projektów.
Publishing for the students living in the iPad era: our view of the industrySebastien Dubuis
Publishing for the iPad generation of students requires some new mindset. How to enhance textbooks to create a lively reading experience? How to seamlessly offer cross devices compatibility?
What do zen practice and Oracle SQL optimization have in common? You’d be surprised! This presentation demonstrates how the zen principles of simplicity, focus, and practice can be applied to your technical skills to make you a calm and fearless SQL optimizer. You will be “enlightened” as to how to find ease with your day-to-day SQL tuning activities.
Updated version of the RecSys TEL lecture I already gave as invited talk in the UK, NL and DE. The conclusion parts is totally new and aligned to the new book on RecSys for Learning at Springer that will appear soon in 2012.
How to use LLMs for creating a content-based recommendation system for entert...mahaffeycheryld
To utilize Large Language Models (LLMs) for content-based recommendation systems in entertainment platforms, follow these steps:
Data Collection: Gather diverse datasets of entertainment content with metadata.
Preprocessing: Clean, tokenize, and encode textual data for model input.
Model Selection: Choose an LLM architecture like GPT-3 and fine-tune it on the dataset.
Feature Extraction: Extract relevant features from the data, such as genre, keywords, and sentiment.
Recommendation Generation: Utilize the fine-tuned LLM to generate personalized recommendations based on user preferences and content features.
Evaluation and Optimization: Assess recommendation quality and iterate for continual improvement.
https://www.leewayhertz.com/build-content-based-recommendation-for-entertainment-using-llms/
Immersive Recommendation Workshop, NYC Media Lab'17Longqi Yang
The rapid evolution of deep learning technologies and the explosion of diverse user interaction traces have brought significant challenges and opportunities to recommendation and personalized systems. In this workshop, we discussed recent trends and techniques in user modeling and presented our work on immersive recommendation systems. These systems learn users’ preferences from diverse digital trace modalities (text, image and unstructured data streams) in a wide range of recommendation domains (creative art, food, news, and events). The workshop included a light tutorial on OpenRec, an open source framework that enables quick prototyping of complex recommender systems via modularization.
This workshop is based on research and development done at Cornell Tech as part of the Connected Experiences Lab, supported by Oath and NSF.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Mendeley: Recommendation Systems for Academic LiteratureKris Jack
I gave this talk to an MSc class about Semantic Technologies at the Technical University of Graz (TUG) on 2012/01/12.
It presents what recommendation systems are and how they are often used before delving into how they are used at Mendeley. Real-world results from Mendeley’s article recommendation system are also presented.
The work presented here has been partially funded by the European Commission as part of the TEAM IAPP project (grant no. 251514) within the FP7 People Programme (Marie Curie).
In this webinar, Prof Hendrik Drachsler will reflect on the process of applying learning analytics solutions within higher education settings, its implications, and the critical lessons learned in the Trusted Learning Research Program. The talk will focus on the experience of edutec.science research collective consisting of researchers from the Netherlands and Germany that contribute to the Trusted Learning Analytics (TLA) research program. The TLA program aims to provide actionable and supportive feedback to students and stands in the tradition of human-centered learning analytics concepts. Thus, the TLA program aims to contribute to unfolding the full potential of each learner. It, therefore, applies sensor technology to support psychomotor as well as web technology to support meta-cognitive and collaborative learning skills with high-informative feedback methods. Prof. Drachsler applies validated measurement instruments from the field of psychometric and investigates to what extent Learning Analytics interventions can reproduce the findings of these instruments. During this webinar, Prof Drachsler will discuss the lessons learned from implementing TLA systems. He will touch on TLA prerequisites like ethics, privacy, and data protection, as well as high informative feedback for psychomotor, collaborative, and meta-cognitive competencies and the ongoing research towards a repository, methods, tools and skills that facilitate the uptake of TLA in Germany and the Netherlands.
Smart Speaker as Studying Assistant by Joao ParganaHendrik Drachsler
The thesis by Joao Pargana followed two main goals, first, a smart speaker application was created to support learners in informal learning processes through a question/answer application. Second, the impact of the application was tested amongst various users by analyzing how adoption and
transition to newer learning procedures can occur.
Dieser Entwurf eines Verhaltenskodex richtet sich an Hochschulen, die mittels Learning Analytics die Qualität des Lernens und Lehrens verbessern wollen. Der Kodex kann als Vorlage zur Erstellung von organisationsspezifischen Verhaltenskodizes dienen. Er sollte an Hochschulen, die Learning Analytics einführen wollen, durch Konsultationen mit allen Interessengruppen überprüft und an die Ziele sowie die bestehende Praxis innerhalb der jeweiligen Hochschulen angepasst werden. Der Kodex wurde auf Grundlage einer Analyse bestehender europäischer Kodizes und der in Deutschland geltenden Rechtsgrundlage vom Innovationsforum Trusted Learning Analytics des hessenweiten Projektes "Digital gestütztes Lehren und Lernen in Hessen" entwickelt.
Abstract (English):
This code of conduct can be used as a template for creating organization-specific codes of conduct in Germany. The Code was developed on the basis of an analysis of existing European codes of conduct and the legal basis for the usage of data in higher education in Germany.
Rödling, S. (2019). Entwicklung einer Applikation zum assoziativen Medien Ler...Hendrik Drachsler
Ziel der vorliegenden Bachelorarbeit ist es, den Einfluss von zusätzlicher am Handgelenk wahr-genommener Vibration in Verbindung mit der visuellen Darstellung eines Lerninhaltes auf denLernerfolg zu messen. Der Lernerfolg wird hierbei durch die Lerngeschwindigkeit sowie denUmfang der Wissenskonsolidierung über die Testreihe definiert. Zu diesem Zweck wurde eine Experimentalstudie zumAssoziativen Lernendurchgeführt. Für die Studie verwendeten 33Probanden eine App, die für die vorliegende Arbeit entwickelt wurde. Im Mittel aller Studiener-gebnisse wurden sowohl für die Lerngeschwindigkeit als auch für die Wissenskonsolidierungbessere Werte erzielt, wenn die Probanden die Möglichkeit hatten, den Lerninhalt sowohl visu-ell als auch haptisch zu erfahren. Die festgestellten Unterschiede des Lernerfolges erreichtenjedoch keine statistische Signifikanz. Die Abweichungen der Ergebnisse nach der Umsetzungder vorgeschlagenen Änderungen am Studiendesign sind abzuwarten. Die Bachelorarbeit ist vor allem für den Bildungsbereich interessant.
The present bachelor thesis aims to measure the influence of vibration perceived at the wrist in connection with the visual representation of learning content on the learning success. The learning success is defined by the learning speed and the extent of knowledge consolidation over the test series. For this purpose, an experimental study on Associative Learning was conducted. For the study, 33 test persons used an app, which was developed for the present work. On average of all study results better values were achieved for both learning speed and knowledge consolidation, if the test persons could experience the learning content both visually and haptically. However, the differences in learning outcomes did not reach statistical significance. The results of the deviations after the implementation of the proposed changes to the study design must be awaited. The Bachelor’s thesis is particularly interesting for the education sector.
E.Leute: Learning the impact of Learning Analytics with an authentic datasetHendrik Drachsler
Nowadays, data sets of the interactions of users and their corresponding demographic data are becoming more and more valuable for companies and academic institutions like universities
when optimizing their key performance indicators. Whether it is to develop a model to predict the optimal learning path for a student or to sell customers additional products, data sets to
train these models are in high demand. Despite the importance and need for big data sets it still has not become apparent to every decision-maker how crucial data sets like these are for the
future success of their operations.
The objective of this thesis is to demonstrate the use of a data set, gathered from the virtual learning environment of a distance learning university, by answering a selection of questions in
Learning Analytics. Therefore, a real-world data set was analyzed and the selected questions were answered by using state-of-the-art machine learning algorithms.
Romano, G. (2019) Dancing Trainer: A System For Humans To Learn Dancing Using...Hendrik Drachsler
Masters thesis by Romano, G., (2019). Dancing is the ability to feel the music and express it in rhythmic movements with the body. But learning how to dance can be challenging because it requires proper coordination and understanding of rhythm and beat. Dancing courses, online courses or learning with free content are ways to learn dancing. However, solutions with human-computer interaction are rare or
missing. The Dancing Trainer (DT) is proposed as a generic solution to fill this gap. For the beginning, only Salsa is implemented, but more dancing styles can be added. The DT uses the Kinect to interact multimodally with the user. Moreover, this work shows that dancing steps can be defined as gestures with the Kinect v2 to build a dancing corpus. An experiment with
25 participants is conducted to determine the user experience, strengths and weaknesses of the DT. The outcome shows that the users liked the system and that basic dancing steps were
learned.
In May 2018, the new General Data Protection Regulation (GDPR) will enter into force in the European Union. This new regulation is considered as the most modern data protection law for Big Data societies of tomorrow. The GDPR will bring major changes to data ownership and the way data can be accessed, processed, stored, and analysed in the European Union. From May 2018 onwards, data subjects gain fundamental rights such as ‘the right to access data’ or ‘the right to be forgotten’. This will force Big Data system designers to follow a privacy-by-design approach for their infrastructures and fundamentally change the way data can be treated in the European Union.
The presentation provides an overview of the Trusted Learning Analytics Programme as it has been recently initiated at the University of Frankfurt and the DIPF research institute in Germany. Educational data is under special focus of the GDPR, as it is considered as highly sensitive like data from a nuclear plant. It shows opportunities and challenges for using educational data for learning analytics purposes under the light of the GDPR 2018.
Fighting level 3: From the LA framework to LA practice on the micro-levelHendrik Drachsler
This presentation explores shortcomings of learning analytics for the wide adoption in educational organisations. It is NOT about ethics and privacy rather than focuses on shortcomings of learning analytics for teachers and students in the classroom (micro-level). We investigated if and to what extend learning analytics dashboards are addressing educational concepts. Map opportunities and challenges for the use of Learning Analytics dashboards for the design of courses, and present an evaluation instrument for the effects of Learning Analytics called EFLA. EFLA can be used to measure the effects of LA tools at the teacher and student side. It is a robust but light (8 items) measurement to quickly investigate the level of adoption of learning analytics in a course (micro-level). The presentation concludes that Learning Analytics is still to much a computer science dicipline that does not fulfill the often claimed position of the middle space between educational and computer science research.
Presentation given at PELARS Policy event, Brussles, 09.11.2016. A follow up op the first LACE Policy event in April 2015. Special focus is on the exploitation and sustainability activities for LACE in the SIG LACE SoLAR.
Dutch Cooking with xAPI Recipes, The Good, the Bad, and the ConsistentHendrik Drachsler
This paper presents the experiences of several Dutch projects in their application of the xAPI standard and different design patterns including the deployment of Learning Record Stores. In this paper we share insights and argue for the formation of an international Special Interest Group on interoperability issues to contribute to the Open Analytics Framework as envisioned by SoLAR and enacted by the Apereo Learning Analytics Initiative. Therefore, we provide an overview of the advantages and disadvantages of implementing the current xAPI standard by presenting projects that applied xAPI in very different ways followed by the lessons learned.
Recommendations for Open Online Education: An Algorithmic StudyHendrik Drachsler
Recommending courses to students in online platforms is studied widely. Almost all studies target closed platforms, that belong to a University or some other educational provider. This makes the course recommenders situation specific. Over the last years, a demand has developed for recommender system that suit open online platforms. Those platforms have some common characteristics, such as the lack of rich user profiles with content metadata. Instead they log user interactions within the platform that can be used for analysis and personalization. In this paper, we investigate how user interactions and activities tracked within open online learning platforms can be used to provide recommendations. We present a study in which we investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. We use data from the OpenU open online learning platform that is in use by the Open University of the Netherlands. The results show that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system proves to outperform the classical approaches on prediction accuracy of recommendations in terms of recall. We conclude that, if the algorithms are chosen wisely, recommenders can contribute to a better experience of learners in open online courses.
Soude Fazeli, Enayat Rajabi, Leonardo Lezcano, Hendrik Drachsler, Peter Sloep
Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learni...Hendrik Drachsler
The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistin-guishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution’s learning support by implementing data and analytics with a view on improving student success. In this presentation, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
DELICATE checklist - to establish trusted Learning AnalyticsHendrik Drachsler
The DELICATE checklist contains eight action points that should be considered by managers and decision makers planning the implementation of Learning Analytics / Educational Data Mining solutions either for their own institution or with an external provider.
The eight points are:
1. Determination: Decide on the purpose of learning analytics for your institution. What aspects of learning or learner services are you trying to improve?
2. Explain: Define the scope of data collection and usage. Who has a need to have access to the data or the results? Who manages the datasets? On what criteria?
3. Legitimate: Explain how you operate within the legal frameworks, refer to the essential legislation. Is the data collection excessive, random, or fit for purpose?
4. Involve: Talk to stakeholders and give assurances about the data distribution and use. Give as much control as possible to data subjects (permission architecture), and provide access to their data for the individuals.
5. Consent: Seek consent through clear consent questions. Provide an opt-out option.
6. Anonymise: De-identify individuals as much as possible, aggregate data into meta-models.
7. Technical aspects: Monitor who has access to data, especially in areas with high staff turn-over. Establish data storage to high security standards.
8. External partners: Make sure externals provide highest data security standards. Ensure data is only used for intended purposes and not passed on to third parties.
We hope that the DELICATE checklist will be a helpful instrument for any educational institution to demystify the ethics and privacy discussions around Learning Analytics. As we have tried to show in this article, there are ways to design and provide privacy conform Learning Analytics that can benefit all stakeholders and keep control with the users themselves and within the established trusted relationship between them and the institution.
Updated Flyer of the LACE project with latest tangible outcomes and collaboration possibilities.
LACE connects players in the fields of Learning Analytics (LA) and Educational Data Mining (EDM) in order to support the development of a European community and share emerging best practices.
Objectives
-------------
• Promote knowledge creation and exchange
• Increase the evidence base about Learning Analytics
• Contribute to the definition of future directions
• Build consensus on pressing topics like data interoperability, data sharing, ethics and privacy, and Learning Analytics supported instructional design
Activities
• Organise events to connect organisations that are conducting LA/EDM research
• Create and curate a knowledge base to capture evidence for the effectiveness of Learning Analytics
• Produce reviews to inform the LACE community about latest developments in the field
Presentation given at Serious Request 2015, #SR15, Heerlen.
Within the Open University we started a 12 hours marathon college, to collect money for the charity action of radiostation 3FM. The collected money will go to the red cross and support young people in conflict areas.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Recommender Systems and Learning Analytics in TEL
1. Recommender Systems and
Learning Analytics in TEL
Hendrik Drachsler
Open University of the Netherlands
2. Hendrik Drachsler
• Assistant professor at the Centre for Learning
Sciences and Technologies (CELSTEC)
• Track record in TEL projects such as
TENCompetence, SC4L, LTfLL, Handover, dataTEL.
• Main research focus:
– Personalization of learning with information
retrieval technologies, recommender systems and
educational datasets
– Visualization of educational data, data mash-up
environments, supporting context-awareness by
data mining
– Social and ethical implications of data mining in
education
• Leader of the dataTEL Theme Team of the
STELLAR network of excellence (join the SIG on
TELeurope.eu)
• Just recently: new alterEGO project granted by the
Netherlands Laboratory for Lifelong Learning (on
limitations of learning analytics in formal and
informal learning)
3. Recommender Systems
and Learning Analytics in TEL
23.07.2011 MUP/PLE lecture series,
Knowledge Media Institute, Open University UK
Hendrik Drachsler
Centre for Learning Sciences and Technology
Open University of the Netherlands 3
4. Goals of the lecture
1. Crash course Recommender Systems (RecSys)
2. Overview of RecSys in TEL
3. Open research issues for RecSys in TEL
4.TEL RecSys and Learning Analytics
4
6. Introduction::Application areas
Application areas
• E-commerce websites (Amazon)
• Video, Music websites (Netflix, last.fm)
• Content websites (CNN, Google News)
• Information Support Systems
Major claims
• Highly application-oriented research area, every domain and
task needs a specific RecSys
• Always build around content or products they never
exist as on their own
6
7. Introduction::Definition
Using the opinions of a community of users to
help individuals in that community to identify more
effectively content of interest from a potentially
overwhelming set of choices.
Resnick & Varian (1997). Recommender Systems, Communications of the ACM, 40(3).
7
8. Introduction::Definition
Using the opinions of a community of users to
help individuals in that community to identify more
effectively content of interest from a potentially
overwhelming set of choices.
Resnick & Varian (1997). Recommender Systems, Communications of the ACM, 40(3).
Any system that produces personalized
recommendations as output or has the effect of
guiding the user in a personalized way to interesting
or useful objects in a large space of possible options.
Burke R. (2002). Hybrid Recommender Systems: Survey and Experiments,
User Modeling & User Adapted Interaction, 12, pp. 331-370.
7
17. Introduction::Example
What did we learn from the small exercise?
• There are different kinds of recommendations
a. People who bought X also bought Y
b. there are more advanced personalized recommendations
• When registering, we have to tell the RecSys what we like
(and what not). Thus, it requires information to offer suitable
recommendations and it learns our preferences.
8
19. Introduction:: The Long Tail
“We are leaving the age of information and
entering the age of recommendation”.
Anderson, C. (2004)
Anderson, C., (2004). The Long Tail. Wired Magazine.
9
22. Introduction:: Age of RecSys?
... another 10 minutes, research on RecSys is
becoming main stream.
Some examples:
– ACM RecSys conference
– ICWSM: Weblog and Social Media
– WebKDD: Web Knowledge Discovery and Data Mining
– WWW: The original WWW conference
– SIGIR: Information Retrieval
– ACM KDD: Knowledge Discovery and Data Mining
– LAK: Learning Analytics and Knowledge
– Educational data mining conference
– ICML: Machine Learning
– ...
... and various workshops, books, and journals.
11
23. Objectives
of RecSys probabilistic combination of
– Item-based method
– User-based method
– Matrix Factorization
– (May be) content-based method
The idea is to pick from my
previous list 20-50 movies that
share similar audience with
“Taken”, then how much I will like
depend on how much I liked those
early movies
– In short: I tend to watch this movie
because I have watched those
movies … or
12
– People who have watched those
movies also liked this movie
24. Objectives::Aims
• Converting Browsers into
Buyers
• Increasing Cross-sales
• Building Loyalty
Foto by markhillary
Schafer, Konstan & Riedel, (1999). RecSys in e-commerce. Proc. of the 1st ACM on
electronic commerce, Denver, Colorado, pp. 158-169.
13
25. Objectives::RecSys Tasks
Find good items
presenting a ranked list of
recommendendations.
probabilistic combination of
– Item-based method
– User-based method
– Matrix Factorization
– (May be) content-based method
Find all good items
user wants to identify all
The idea is to pick from my
items that might be previous list 20-50 movies that
share similar audience with
interesting, e.g. medical “Taken”, then how much I will like
depend on how much I liked those
or legal cases early movies
– In short: I tend to watch this movie
because I have watched those
Herlocker, Konstan, Borchers, & Riedl (2004). Evaluating Collaborative Filtering
movies … or
Recommender Systems. ACM Transactions on–Informationhave watched those pp. 5-53.
14
People who Systems, 22(1),
movies also liked this movie
26. Objectives::RecSys Tasks
Find good items Receive sequence of items
presenting a ranked list of sequence of related items is
recommendendations. recommended to the user,
e.g. music recommender
probabilistic combination of
– Item-based method
– User-based method
– Matrix Factorization
Find all good items Annotation in context
– (May be) content-based method
user wants to identify all predicted usefulness of an
items that might be item that pick from mythatis currently
The idea is to the user
previous list 20-50 movies
interesting, e.g. medical viewing, e.g. linkslike
share similar audience with within a
“Taken”, then how much I will
or legal cases websitehow much I liked those
depend on
early movies
– In short: I tend to watch this movie
because I have watched those
Herlocker, Konstan, Borchers, & Riedl (2004). Evaluating Collaborative Filtering
movies … or
Recommender Systems. ACM Transactions on–Informationhave watched those pp. 5-53.
14
People who Systems, 22(1),
movies also liked this movie
27. Objectives::RecSys Tasks
Find good items Receive sequence of items
presenting a ranked list of sequence of related items is
recommendendations. recommended to the user,
e.g. music recommender
There are more tasks available... of
probabilistic combination
– Item-based method
– User-based method
– Matrix Factorization
Find all good items Annotation in context
– (May be) content-based method
user wants to identify all predicted usefulness of an
items that might be item that pick from mythatis currently
The idea is to the user
previous list 20-50 movies
interesting, e.g. medical viewing, e.g. linkslike
share similar audience with within a
“Taken”, then how much I will
or legal cases websitehow much I liked those
depend on
early movies
– In short: I tend to watch this movie
because I have watched those
Herlocker, Konstan, Borchers, & Riedl (2004). Evaluating Collaborative Filtering
movies … or
Recommender Systems. ACM Transactions on–Informationhave watched those pp. 5-53.
14
People who Systems, 22(1),
movies also liked this movie
28. RecSys Technologies
1. Predict how much a user
may like a certain product
2. Create a list of Top-N
best items
3. Adjust its prediction
based on feedback of the
target user and like-
minded users
Hanani et al., (2001). Information Filtering: Overview of Issues, Research and Systems",
User Modeling and User-Adapted Interaction, 11.
15
29. RecSys Technologies
1. Predict how much a user
may like a certain product
2. Create a list of Top-N
best items
3. Adjust its prediction
based on feedback of the Just some examples
target user and like- there are more
minded users technologies available.
Hanani et al., (2001). Information Filtering: Overview of Issues, Research and Systems",
User Modeling and User-Adapted Interaction, 11.
15
30. Technologies::Collaborative filtering
User-based filtering
(Grouplens, 1994)
Take about 20-50 people who share
similar taste with you, afterwards
predict how much you might like an
item depended on how much the others
liked it.
You may like it because your
“friends” liked it.
16
31. Technologies::Collaborative filtering
User-based filtering Item-based filtering
(Grouplens, 1994) (Amazon, 2001)
Take about 20-50 people who share Pick from your previous list 20-50 items
similar taste with you, afterwards that share similar people with “the
predict how much you might like an target item”, how much you will like the
item depended on how much the others target item depends on how much the
liked it. others liked those earlier items.
You may like it because your You tend to like that item because
“friends” liked it. you have liked those items.
16
32. Technologies::Content-based filtering
Information needs of user and characteristics of items are
represented in keywords, attributes, tags that describe
past selections, e.g., TF-IDF.
17
33. Technologies::Hybrid RecSys
Combination of techniques to overcome
disadvantages and advantages of single techniques.
Advantages Disadvantages
probabilistic combination of
– Item-based method
• No content analysis • Cold-start problem
– User-based method
– Matrix Factorization
• Quality improves • Over-fitting
– (May be) content-based method
• No cold-start problem • New user / item problem
The idea is to pick from my
• No new user / item • Sparsity
previous list 20-50 movies that
share similar audience with
problem “Taken”, then how much I will like
depend on how much I liked those
early movies
– In short: I tend to watch this movie
because I have watched those
movies … or
18
– People who have watched those
movies also liked this movie
34. Technologies::Hybrid RecSys
Combination of techniques to overcome
disadvantages and advantages of single techniques.
Advantages Disadvantages
probabilistic combination of
– Item-based method
• No content analysis • Cold-start problem
– User-based method
– Matrix Factorization
• Quality improves • Over-fitting
– (May be) content-based method
• No cold-start problem • New user / item problem
The idea is to pick from my
• No new user / item • Sparsity
previous list 20-50 movies that
share similar audience with
problem “Taken”, then how much I will like
Just some examples there
depend on how much I liked those
early movies
are more (dis)advantages
– In short: I tend to watch this movie
because I have watched those
18
movies … or
available.
– People who have watched those
movies also liked this movie
35. Evaluation
of RecSys
probabilistic combination of
– Item-based method
– User-based method
– Matrix Factorization
– (May be) content-based method
The idea is to pick from my
previous list 20-50 movies that
share similar audience with
“Taken”, then how much I will like
depend on how much I liked those
early movies
– In short: I tend to watch this movie
because I have watched those
movies … or
19
– People who have watched those
movies also liked this movie
36. Evaluation::General idea
Most of the time based on performance measures
(“How good are your recommendations?”)
For example:
•Predict what rating will a user give an item?
•Will the user select an item?
•What is the order of usefulness of items to a user?
Herlocker, Konstan, Riedl (2004). Evaluating Collaborative Filtering Recommender
Systems. ACM Transactions on Information Systems, 22(1), 5-53.
20
39. Evaluation::Metrics
Precision – The portion of
recommendations that were
successful. (Selected by the
algorithm and by the user)
Recall – The portion of relevant
items selected by algorithm
compared to a total number of
relevant items available.
F1 - Measure balances Precision
and Recall into a single
measurement.
Gunawardana, A., Shani, G., (2009). A Survey of Accuracy Evaluation Metrics of
Recommendation Tasks, Journal of Machine Learning Research, 10(Dec):2935−2962,
2009. 23
40. Evaluation::Metrics
Precision – The portion of
recommendations that were
successful. (Selected by the
algorithm and by the user)
Recall – The portion of relevant
items selected by algorithm
compared to a total number of
relevant items available.
F1 - Measure balances Precision
and Recall into a single
measurement.
Gunawardana, A., Shani, G., (2009). A Survey of Accuracy Evaluation Metrics of
Recommendation Tasks, Journal of Machine Learning Research, 10(Dec):2935−2962,
2009. 23
41. Evaluation::Metrics
Precision – The portion of
recommendations that were
successful. (Selected by the
algorithm and by the user)
Recall – The portion of relevant
items selected by algorithm
compared to a total number of
relevant items available.
F1 - Measure balances Precision
and Recall into a single
measurement.
Gunawardana, A., Shani, G., (2009). A Survey of Accuracy Evaluation Metrics of
Recommendation Tasks, Journal of Machine Learning Research, 10(Dec):2935−2962,
2009. 23
42. Evaluation::Metrics
Precision – The portion of
recommendations that were
successful. (Selected by the
algorithm and by the user)
Recall – The portion of relevant
items selected by algorithm
compared to a total number of
relevant items available.
F1 - Measure balances Precision Just some examples there
and Recall into a single are more metrics available
measurement. like MAE, RSME.
Gunawardana, A., Shani, G., (2009). A Survey of Accuracy Evaluation Metrics of
Recommendation Tasks, Journal of Machine Learning Research, 10(Dec):2935−2962,
2009. 23
43. Evaluation::Metrics
5
Conclusion:
4
Pearson is better
RMSE
than Cosine, 3
Pearson
because less 2
errors in predicting Cosine
1
TOP-N items. 0
Netflix BookCrossing
Gunawardana, A., Shani, G., (2009). A Survey of Accuracy Evaluation Metrics of
Recommendation Tasks, Journal of Machine Learning Research, 10(Dec):2935−2962,
2009. 24
44. Evaluation::Metrics
5
Conclusion:
4
Pearson is better
RMSE
than Cosine, 3
Pearson
because less 2
errors in predicting Cosine
1
TOP-N items. 0
Netflix BookCrossing
News Story Clicks
Conclusion: 80%
Cosine better than Precision
60%
Pearson, because
40%
of higher precision
20%
and recall value on
TOP-N items. 0%
5% 10% 15% 20% 25% 30% 35% 40%
Recall
Gunawardana, A., Shani, G., (2009). A Survey of Accuracy Evaluation Metrics of
Recommendation Tasks, Journal of Machine Learning Research, 10(Dec):2935−2962,
2009. 24
45. RecSys::TimeToThink
What do you expect that a RecSys in a
MUP/PLE should do with respect to ...
• Aims
• Tasks
• Technology
Blackmore’s custom-built LSD Drive
• Evaluation http://www.flickr.com/photos/
rootoftwo/
25
46. Goals of the lecture
1. Crash course Recommender Systems (RecSys)
2. Overview of RecSys in TEL
3. Open research issues for RecSys in TEL
4.TEL RecSys and Learning Analytics
26
48. TEL RecSys::Definition
Using the experiences of a community of
learners to help individual learners in that
community to identify more effectively learning
content from a potentially overwhelming set of
choices.
Extended definition by Resnick & Varian (1997). Recommender Systems, Communications of the
ACM, 40(3).
28
56. TEL RecSys:: Technologies
RecSys Task:
Find good items
Hybrid RecSys:
•Content-based on
interests
•Collaborative filtering
33
57. TEL RecSys::Tasks
Find good items
e.g. relevant items for a learning
task or a learning goal
The idea is to pick from my
previous list 20-50 movies that
share similar audience with
“Taken”, then how much I will like
depend on how much I liked those
early movies
– In short: I tend to watch this movie
because I have watched those
Drachsler, H., Hummel, H., Koper, R., (2009). Identifying the goal, user model and conditions of
movies … or
recommender systems for formal and informal–learning. Journal watched those
34
People who have of Digital Information. 10(2).
movies also liked this movie
58. TEL RecSys::Tasks
Find good items
e.g. relevant items for a learning
task or a learning goal
Receive sequence of items
e.g. recommend a learning path
to achieve a certain
competence
The idea is to pick from my
previous list 20-50 movies that
share similar audience with
“Taken”, then how much I will like
depend on how much I liked those
early movies
– In short: I tend to watch this movie
because I have watched those
Drachsler, H., Hummel, H., Koper, R., (2009). Identifying the goal, user model and conditions of
movies … or
recommender systems for formal and informal–learning. Journal watched those
34
People who have of Digital Information. 10(2).
movies also liked this movie
59. TEL RecSys::Tasks
Find good items
e.g. relevant items for a learning
task or a learning goal
Receive sequence of items
e.g. recommend a learning path
to achieve a certain
competence
Annotation in context The idea is to pick from my
e.g. take into account location, previous list 20-50 movies that
share similar audience with
time, noise level, prior “Taken”, then how much I will like
knowledge, peers around depend on how much I liked those
early movies
– In short: I tend to watch this movie
because I have watched those
Drachsler, H., Hummel, H., Koper, R., (2009). Identifying the goal, user model and conditions of
movies … or
recommender systems for formal and informal–learning. Journal watched those
34
People who have of Digital Information. 10(2).
movies also liked this movie
60. Evaluation
of TEL
RecSys probabilistic combination of
– Item-based method
– User-based method
– Matrix Factorization
– (May be) content-based method
The idea is to pick from my
previous list 20-50 movies that
share similar audience with
“Taken”, then how much I will like
depend on how much I liked those
early movies
– In short: I tend to watch this movie
because I have watched those
movies … or
35
– People who have watched those
movies also liked this movie
62. TEL RecSys::Review study
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011).
Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci,
L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415).
Berlin: Springer. 36
63. TEL RecSys::Review study
Conclusions:
Half of the systems (11/20) still at design or prototyping stage
only 8 systems evaluated through trials with human users.
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011).
Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci,
L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415).
Berlin: Springer. 36
64. Thus...
“The performance results
of different research
efforts in recommender
systems are hardly
comparable.”
(Manouselis et al., 2010)
Kaptain Kobold
http://www.flickr.com/photos/
kaptainkobold/3203311346/
37
65. Thus...
TEL recommender
“The performance results
experiments lack
of different research
efforts in recommender
transparency. They need
systems are hardly
to be repeatable to test:
comparable.”
• Validity
•(Manouselis et al., 2010)
Verification
• Compare results Kaptain Kobold
http://www.flickr.com/photos/
kaptainkobold/3203311346/
37
67. TEL RecSys::Evaluation/datasets
Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Beham, G.,
Lindstaedt, S., Stern, H., Friedrich, M., & Wolpers, M. (2010). Issues and Considerations
regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning.
Presentation at the 1st Workshop Recommnder Systems in Technology Enhanced Learning
(RecSysTEL) in conjunction with 5th European Conference on Technology Enhanced
Learning (EC-TEL 2010): Sustaining TEL: From Innovation to Learning and Practice.
September, 28, 2010, Barcelona, Spain. 38
68. Evaluation::Metrics
MAE – Mean Absolute Error:
Deviation of recommendations
from the user-specified ratings.
The lower the MAE, the more
accurately the RecSys predicts user
ratings.
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., Beham, G., Duval, E.,
(2011). Dataset-driven Research for Improving Recommender Systems for Learning. Learning
Analytics & Knowledge: February 27-March 1,39 2011, Banff, Alberta, Canada
69. Evaluation::Metrics
MAE – Mean Absolute Error:
Deviation of recommendations
from the user-specified ratings.
The lower the MAE, the more
accurately the RecSys predicts user
ratings.
Outcomes:
Tanimoto similarity +
item-based CF was
the most accurate.
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., Beham, G., Duval, E.,
(2011). Dataset-driven Research for Improving Recommender Systems for Learning. Learning
Analytics & Knowledge: February 27-March 1,39 2011, Banff, Alberta, Canada
70. Evaluation::Metrics
MAE – Mean Absolute Error:
Deviation of recommendations
from the user-specified ratings.
The lower the MAE, the more
accurately the RecSys predicts user
ratings.
Outcomes:
•User-based CF Algorithm that
predicts the top 10 most relevant
Outcomes:
items for a user has a F1 score
Tanimoto similarity +
of almost 30%.
item-based CF was
•the most accurate.
Implicit ratings like download
rates, bookmarks can
successfully used in TEL.
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., Beham, G., Duval, E.,
(2011). Dataset-driven Research for Improving Recommender Systems for Learning. Learning
Analytics & Knowledge: February 27-March 1,39 2011, Banff, Alberta, Canada
72. TEL RecSys::Evaluation
1. Accuracy
2. Coverage
3. Precision
Combined approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
40
73. TEL RecSys::Evaluation
1. Accuracy
2. Coverage
3. Precision
1. Effectiveness of learning
2. Efficiency of learning
3. Drop out rate
4. Satisfaction
Combined approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
40
74. TEL RecSys::Evaluation
1. Accuracy 1. Reaction of learner
2. Coverage 2. Learning improved
3. Precision 3. Behaviour
4. Results
1. Effectiveness of learning
2. Efficiency of learning
3. Drop out rate
4. Satisfaction
Combined approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
40
75. Goals of the lecture
1. Crash course Recommender Systems (RecSys)
2. Overview of RecSys in TEL
3. Open research issues for RecSys in TEL
4.TEL RecSys and Learning Analytics
41
76. TEL RecSys::Open issues
1. Evaluation of TEL RecSys
2. Publicly available datasets
3. Comparable experiments
4. Body of knowledge
5. Privacy and data protection
6. Design learning driven RecSys
42
77. Goals of the lecture
1. Crash course Recommender Systems (RecSys)
2. Overview of RecSys in TEL
3. Open research issues for RecSys in TEL
4.TEL RecSys and Learning Analytics
43
85. Learning Analytics::TimeToThink
• Consider the Learning Analytics
framework and imagine some great TEL
RecSys that could support you in your
stakeholder role
alternatively
• Name a learning task where a TEL
RecSys would be useful for.
45
86. Thank you for attending this lecture!
This silde is available at:
http://www.slideshare.com/Drachsler
Email: hendrik.drachsler@ou.nl
Skype: celstec-hendrik.drachsler
Blogging at: http://www.drachsler.de
Twittering at: http://twitter.com/HDrachsler
46