Collaboraive sharing of molecules and data in the mobile ageSean Ekins
An overview of using collaborative software in small and large scale collaborations in drug discovery. A focus on Tuberculosis. Also analysis of collaboration and mobile apps for science
Collaboraive sharing of molecules and data in the mobile ageSean Ekins
An overview of using collaborative software in small and large scale collaborations in drug discovery. A focus on Tuberculosis. Also analysis of collaboration and mobile apps for science
Occupabilità e competitività come muoversi da protagonista nel mondo che cambia.Carlo Colomba
Sono le persone a subire le conseguenze della crisi economica in atto. Ma i veri
processi di cambiamento non possono prescindere dall’impegno e dalla
responsabilità personali. Rispetto ai coetanei di altri Paesi i nostri giovani incontrano il lavoro in età troppo avanzata e, per di più, con conoscenze poco spendibili anche per l’assenza di un vero contatto con il mondo del lavoro in ragione del noto pregiudizio che vuole che chi studia non lavori e che chi lavora non studi. Una promozione della cultura del lavoro e dell’organizzazione aziendale porterebbe benefici sia per i lavoratori che per le imprese, ma l’Italia rimane agli ultimi posti in Europa per quanto riguarda la
partecipazione della popolazione ad attività di formazione continua e per l’alternanza scuola/lavoro. Si tratta di promuovere una cultura dell’apprendimento permanente tra i giovani e gli adulti rimodellando tempi, contenuti e modalità di coinvolgimento all’interno di un sistema di certificazione dei saperi e delle competenze possedute funzionale ad un reale aumento dell’occupabilità a supporto dei momenti di transizione nella vita e nel lavoro.
Upping engagement with digital resourcesLis Parcell
Slide deck used as the basis for a Jisc session at the East Anglian Learning Resources forum held at the ACER offices, St Ives on 4 March 2016. Participants discussed how their learners discover resources, the challenges they face in promoting digital resources and prioritised activities which they felt were most important and/or required further support.
Some additional information was provided on some lesser-known digital resources of possible interest to participants.
The slide deck is licensed CC BY-NC-ND except where shown on individual slides.
Recent Research and Developments on Recommender Systems in TELHendrik Drachsler
Presentation given at the Learning Network seminar series at CELSTEC. Special guest was Wolfgang Reinhardt who provided his view on data science in relation to awareness improvement of knowledge workers.
Occupabilità e competitività come muoversi da protagonista nel mondo che cambia.Carlo Colomba
Sono le persone a subire le conseguenze della crisi economica in atto. Ma i veri
processi di cambiamento non possono prescindere dall’impegno e dalla
responsabilità personali. Rispetto ai coetanei di altri Paesi i nostri giovani incontrano il lavoro in età troppo avanzata e, per di più, con conoscenze poco spendibili anche per l’assenza di un vero contatto con il mondo del lavoro in ragione del noto pregiudizio che vuole che chi studia non lavori e che chi lavora non studi. Una promozione della cultura del lavoro e dell’organizzazione aziendale porterebbe benefici sia per i lavoratori che per le imprese, ma l’Italia rimane agli ultimi posti in Europa per quanto riguarda la
partecipazione della popolazione ad attività di formazione continua e per l’alternanza scuola/lavoro. Si tratta di promuovere una cultura dell’apprendimento permanente tra i giovani e gli adulti rimodellando tempi, contenuti e modalità di coinvolgimento all’interno di un sistema di certificazione dei saperi e delle competenze possedute funzionale ad un reale aumento dell’occupabilità a supporto dei momenti di transizione nella vita e nel lavoro.
Upping engagement with digital resourcesLis Parcell
Slide deck used as the basis for a Jisc session at the East Anglian Learning Resources forum held at the ACER offices, St Ives on 4 March 2016. Participants discussed how their learners discover resources, the challenges they face in promoting digital resources and prioritised activities which they felt were most important and/or required further support.
Some additional information was provided on some lesser-known digital resources of possible interest to participants.
The slide deck is licensed CC BY-NC-ND except where shown on individual slides.
Recent Research and Developments on Recommender Systems in TELHendrik Drachsler
Presentation given at the Learning Network seminar series at CELSTEC. Special guest was Wolfgang Reinhardt who provided his view on data science in relation to awareness improvement of knowledge workers.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
Lesson 2 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
Over the past 10 years, research systems have evolved from systems that focused on how to structure and record information on research, to systems capable of allowing significant insights to be derived based upon years of high quality information. In 2015, the maturity of the information now collected within many Current Research Information Systems, and the insights that this can provide is of equal or greater value than the insights that could be gleaned from established externally provided research metrics platforms alone. The ability to intersect these external and internal worlds provides new levels of strategic insight not previously available. With the addition of platforms that track altmetrics, and their ability to connect university publications data with a constant flow of real time attention level metrics, an image of a dynamic network of systems emerges, connected together by ever turning ‘cogs’ pushing and translating information. Add to this, the success of ORCID as pervasive researcher identifier infrastructure, and CASRAI as the emerging social contract for information exchange, and it becomes possible to extend this network back from the systems that track and record research information, through to the platforms through which research knowledge is created. The ‘Mechanics’ of this network of systems is more than just getting the ‘plumbing’ right. As research information moves through the network, its audience and purpose changes, the requirements for contextual metadata can also change. This presentation will explore the lived experience of Research Data Mechanics at Digital Science though illustrating how connections between Figshare, Altmetric, Symplectic Elements, and Dimensions can both enhance research system capability and reduce the burden on researchers, and research administration.
FAIRness Assessment of the Library of Integrated Network-based Cellular Signa...Kathleen Jagodnik
The FAIR Guiding Principles facilitate the Findability, Accessibility, Interoperability, and Reusability of digital resources. The Library of Integrated Network-based Cellular Signatures (LINCS) Project has sought to implement the FAIR principles in the provision of its resources in order to optimize usability. We have surveyed the FAIR principles and are implementing specific facets within the LINCS resources. Subsequently, with reference to the literature and other efforts to measure FAIRness, we are developing quantitative metrics to assess the FAIRness of each dataset and resource in order to provide users with objective measures of the characteristics of the LINCS project. Assessing and improving the FAIRness of LINCS is an ongoing effort by our team that will benefit from community input to ensure that all LINCS users are optimally engaged with this resource.
This presentation was provided by Kristi Holmes of Northwestern University during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
An Empirical Study of the Applications of Classification Techniques in Studen...IJERA Editor
University servers and databases store a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. main problem that faces any system administration or any users is data increasing per-second, which is stored in different type and format in the servers, learning about students from a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. Graduation and academic information in the future and maintaining structure and content of the courses according to their previous results become importance. The paper objectives are extract knowledge from incomplete data structure and what the suitable method or technique of data mining to extract knowledge from a huge amount of data about students to help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were registered and stored. The classification task is used, the classifier tree C4.5, to predict the final academic results, grades, of students. We use classifier tree C4.5 as the method to classify the grades for the students .The data include four years period [2006-2009]. Experiment results show that classification process succeeded in training set. Thus, the predicted instances is similar to the training set, this proves the suggested classification model. Also the efficiency and effectiveness of C4.5 algorithm in predicting the academic results, grades, classification is very good. The model also can improve the efficiency of the academic results retrieving and evidently promote retrieval precision.
Similar to Issues and Considerations regarding Sharable Data Sets for Recommender Systems in TEL (20)
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.
2. Free
the data
by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l
2
3. Why ?
by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l
2
4. Because we
will get new
insights
by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l
2
5. Issues and Considerations regarding Sharable Data
Sets for Recommender Systems in TEL
29.09.2010 RecSysTEL workshop at RecSys and ECTEL conference, Barcelona, Spain
Hendrik Drachsler #dataTEL
Centre for Learning Sciences and Technology
@ Open University of the Netherlands 3
6. What is dataTEL
• dataTEL is a Theme Team funded by the
STELLAR network of excellence.
• It address 2 STELLAR Grand Challenges
1. Connecting Learner
2. Contextualisation
4
9. The TEL recommender
are a bit like this...
We need to select for each application an
appropriate recsys that fits its needs.
6
10. But...
“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/
7
11. But...
The TEL recommender
“The performance results
experiments lack
of different research
transparency. They need
efforts in recommender
to be repeatable to test:
systems are hardly
comparable.”
• Validity
• Verificationet al., 2010)
(Manouselis
• Compare results Kaptain Kobold
http://www.flickr.com/photos/
kaptainkobold/3203311346/
7
15. The Long Tail of Learning
Graphic Wilkins, D., (2009); Long10 concept Anderson, C. (2004)
tail
16. The Long Tail of Learning
Formal Data
Informal Data
Graphic Wilkins, D., (2009); Long10 concept Anderson, C. (2004)
tail
17. Guidelines for Data Set
Aggregation
Justin Marshall, Coded Ornament by
rootoftwo
http://www.flickr.com/photos/rootoftwo/
267285816
11
18. Guidelines for Data Set
Aggregation
1. Create a data set that
realistically reflects the
variables of the learning
setting.
Justin Marshall, Coded Ornament by
rootoftwo
http://www.flickr.com/photos/rootoftwo/
267285816
11
19. Guidelines for Data Set
Aggregation
1. Create a data set that
realistically reflects the
variables of the learning
setting.
2. Use a sufficiently large
set of user profiles
Justin Marshall, Coded Ornament by
rootoftwo
http://www.flickr.com/photos/rootoftwo/
267285816
11
20. Guidelines for Data Set
Aggregation
1. Create a data set that
realistically reflects the
variables of the learning
setting.
2. Use a sufficiently large
set of user profiles
3. Create data sets that
are comparable to others
Justin Marshall, Coded Ornament by
rootoftwo
http://www.flickr.com/photos/rootoftwo/
267285816
11
21. Guidelines for Data Set
Aggregation
1. Create a data set that
realistically reflects the
variables of the learning
setting.
2. Use a sufficiently large
set of user profiles
3. Create data sets that
are comparable to others
Justin Marshall, Coded Ornament by
rootoftwo
http://www.flickr.com/photos/rootoftwo/
267285816
11
22. dataTEL::Objectives
Standardize research on
recommender systems in TEL
Five core questions:
1.How can data sets be shared according to privacy and legal
protection rights?
2.How to development a policy to use and share data sets?
3.How to pre-process data sets to make them suitable for
other researchers?
4.How to define common evaluation criteria for TEL
recommender systems?
5.How to develop overview methods to monitor the
performance of TEL recommender systems on data sets?
12
26. 1. Protection Rights
OVERSHARING
Were the founders of PleaseRobMe.com actually
allowed to grab the data from the web and present it
in that way?
13
27. 1. Protection Rights
OVERSHARING
Were the founders of PleaseRobMe.com actually
allowed to grab the data from the web and present it
in that way?
Are we allowed to use data from social handles and
reuse it for research purposes?
13
28. 1. Protection Rights
OVERSHARING
Were the founders of PleaseRobMe.com actually
allowed to grab the data from the web and present it
in that way?
Are we allowed to use data from social handles and
reuse it for research purposes?
and there is more company protection rights!
13
36. 3. Pre-Process Data Sets
For informal data sets:
1. Collect data
2. Process data
3. Share data
For formal data sets
from LMS:
1. Data storing scripts
2. Anonymisation scripts
15
38. 4. Evaluation Criteria
1. Accuracy
2. Coverage
3. Precision
Combine approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
16
39. 4. Evaluation Criteria
1. Accuracy
2. Coverage
3. Precision
1. Effectiveness of learning
2. Efficiency of learning
3. Drop out rate
4. Satisfaction
Combine approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
16
40. 4. Evaluation Criteria
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
Combine approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
16
41. 4. Evaluation Criteria
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
Combine approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
16
42. 4. Evaluation Criteria
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
Combine approach by Kirkpatrick model by
Drachsler et al. 2008 Manouselis et al. 2010
16
43. 5. Data Set Framework to Monitor
Performance
Datasets
Formal Informal
Data A Data B Data C
Algorithms: Algorithms: Algorithms:
Algoritmen A Algoritmen D Algoritmen B
Algoritmen B Algoritmen E Algoritmen D
Algoritmen C
Models: Models: Models:
Learner Model A Learner Model C Learner Model A
Learner Model B Learner Model E Learner Model C
Measured attributes: Measured attributes: Measured attributes:
Attribute A Attribute A Attribute A
Attribute B Attribute B Attribute B
Attribute C Attribute C Attribute C
17
44. Join us for a Coffee ...
http://www.teleurope.eu/pg/groups/9405/datatel/
18
45. Upcoming event ...
Workshop: dataTEL- Data Sets for Technology Enhanced Learning
Date: March 30th to March 31st, 2011
Location: Ski resort La Clusaz in the French Alps, Massif des
Aravis
Funding: Food and lodging for 3 nights for 10 selected participants
Submissions: For being funded, please send extended abstracts to
http://www.easychair.org/conferences/?conf=datatel2011
Deadline for submissions: October 25th, 2010
CfP: http://www.teleurope.eu/pg/pages/view/46082/
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46. Many thanks for your interests
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
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