© role-project.eu

May I suggest? Three PLE recommender strategies in comparison

PLE Conference, Southampton, July 11-13, 2011

Felix Mödritscher (speaker), Barbara KrumayVienna Univ. of Economics &Business, Austria

Sten Govaerts, Erik DuvalKatholieke Universiteit Leuven, Belgium

Ingo DahnUniversity of Koblenz-Landau, Germany

Sandy El Helou, Denis GilletEPFL, Switzerland

Alexander Nussbaumer, Dietrich Albert Graz University of Technology, Austria

Carsten UllrichShanghai Jiao Tong University, China

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Agenda

1. PLEs and recommendations – why?

2. Three approaches from the ROLE project

a. Federated Search Widget

b. Community-based PLE Recommender

c. Psycho-pedagogical Recommender

3. Comparison of the PLE recommenders

1. Personal Learning Environments (PLEs)

PLE = “set of tools, services, and artefactsgathered from various contexts andto be used by learners” [Henri et al., 08]

Characteristics of PLE-based activities: several actors (different roles) ... ... use technology (tools) to ... ... connect to learner networks and ... ... collaborate on shared artefacts ... ... in order to achieve common goals. [Wild, 09]

Problems: Learners/teachers have varying attitudes and skills in using ICT! can cause negative feelings or states (frustration, distraction, etc.); hindering to proceed with learning or failing to achieve goals[Windschitl & Sahl, 02; Nguyen-Ngoc & Law, 08]

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1. PLEs and Recommendations

Recommendations are necessary “if users have to make choices without sufficient personal experiences of alternatives” [Resnick & Varian, 97]

For TEL: Examples described in the RecSysTEL workshop proceedings!

For PLEs:

1. Pre-given PLE designs for specific needs

2. Possible PLE entities (artefacts, tools, peers) helpful for a specific situation

Recommendations are a powerful instrument for empowering learners to design their PLEs and use technology for learning...

However: Different solution approaches driven by different disciplines...

And: CF techniques not sufficient! (global vs. local top-n)

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2. Approach 1: Federated search widget ‘Binocs’

Aggregate heterogeneous resources from different (social media) repositories

Save, share, assess, and repurpose resources according to user’s interests

Actions taken into account: select resource, like/dislike, preview

Learning/social context derived from course

Forward contextual data to a recommender system (3A contextual ranking service, Graaasp [El Helou et al., 09])

Ranking according to previous interactions and relevance to search query

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http://widgetstore.role-demo.de/content/binocs

2. Approach 2: Community-based recommender ‘PLEShare’

Practice sharing repository on the Web; to be integrated into PLE solutions (Web-API)

Idea: users share PLE experiences voluntarily Two demos: (a) PLEShare widget, (b) PAcMan add-on PAcMan: allows designing tool bundles in the form of

tagged bookmarking lists (=activities); simple features for sharing such activities and retrieving/reusing them

Shared data used for generatingtwo kinds of recommendations:(1) activity patterns for starting newactivities [‘Pattern Store’](2) top-n PLE items (artefacts,tools, peers) for a specific context[no explicit feature but availablevia Web-API]

Techniques: CF, clustering

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https://addons.mozilla.org/en-US/firefox/addon/176479

http://teldev.wu.ac.at/pleshare/api/

2. Approach 3: Psycho-pedagogical recommender

Developed according to theoretical models (self-regulated learning) and relevant taxonomies [Fruhmann et al., 10]

Based on learning goals and competences (learner monitoring and questionnaires)

Realised as widget for providing:(1) support for planning new activities;(2) guidance for ongoing activities; to findappropriate resources (artifacts, tools, peers)

Additional features planned: allowing learners to give feedback on recommendations (implicitly through usage data); provision of explanations; visual feedback on planned and completed activities

Techniques: rule/model-based recommender Remark: no full-featured prototype available

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http://widgetstore.role-demo.de/content/navigation-tool-widget

3. Comparison of our PLE recommenders

11-13/07/2011

PLE Conference,Southampton, 2011 8/9 , © role-project.eu

Binocs widget PLEShare PP recommender

recommender strategy

CF, PageRank-like & content-based

CF & IR/clustering (cliques, topics, ...)

rule/profile-based (competences)

data & data gathering

on entering search terms, automated

tagged bookmarks, voluntarily shared

questionnaires, automated (profile)

estimated accuracy

high (works well in specialized scope; fallback through IR)

average (requires ‘initialization’, cf. cold start & sparsity)

average (rules and profile must be given)

PLE scenario support & usability

average (PLE design phase not considered)

good (currently only focus on PLE design); usable prototypes

good; restricted to pre-def. domains; no cold-start problem

privacy concerns

sufficient anonymization

privacy statement, anonymized activity recordings (=patterns)

raw usage data not used; user profiles not addressed yet

preliminary experiences

preferences for Google results; uptake in business setting better

three studies; works but requires pilot users sharing patterns (e.g. teachers)

internal evaluations; efforts to integrate new data; requires modelling expertise

Please vote for our mediacast

if you like the idea of PLE practice sharing!

http://vimeo.com/groups/ple2011/videos/25817690

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Thanks for your attention!