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Combining Content Analytics and Activity Tracking
to Identify User Interests and Enable Knowledge Discovery
Andrii Vozniuk, María Jesús Rodríguez-Triana, Adrian Holzer, Denis Gillet
The copyright of images belongs to their authors. I will remove them on demand. Contact me at [email protected]
UMAP PALE, Halifax, July 2016
Paper: https://goo.gl/5cJsSK
REACT - EPFL - Lausanne
REACT=
Coordination & Interaction Systems Group
SpeakUp
is a co-located social media to improve audience interaction
speakup.info
graasp.euGraaspa social media used as a personal and collaborative learning environment
Inquiry Learning SpacesTeachers construct courses for their students by finding and
structuring relevant content
Content in Graasp
Teachers would like to benefit from relevant
content uploaded by others
Students would like to get relevant contenteven when not included
How to suggest relevant content to users ?& keep them in control
Approach
“Learner-content interaction is a defining characteristic of education …”
M. G. Moore. Editorial: Three types of interaction.The American Journal of Distance Education, 3(2):1–6, 1989.
”… it is the process of intellectually interacting with content that results in changes in the learner’s understanding, the learner’s perspective, or the cognitive structures of the learner’s mind”
Recommenders for LearningA review of 82 recommenders for learning [Drachsler et al 2015]
Discovering by the instructors relevant learning resources used by students when learning, that are not part of the materials provided by the instructor [Zaldivar et. al. 2011]• Considered present terms to describe the content• TF-IDF based on terms from the content• Looked at one type of interaction (visit)• No possibility to adjust recommendations by the user
Personalized recommendations of relevant knowledge assets based on user interactions with content [El Helou et. al. 2010]• Built user-content graph based on interactions• Used modified PageRank to get relevant items• Considered multiple types of interactions• Did not look inside of the content• No possibility to adjust recommendations by the user
No explicit identification of interests. No control over them.
RecordUser-
ContentInteractions
BuildUser
InterestsProfile
ProvideRecommendations
ExtractConceptsfrom theContent
Our Approach
Extracting ConceptsExtracted
TextContent
Items on platform
Binary Text File
.pdf .docx
Imagewith text
.png .jpg .tiff
Image
Audio
Video
Content Extraction
Plain Text File
Optical Character
Recognition
Speech-To-Text
Visual Image Recognition
Visual Video Recognition
Content Analysis
Content and ConceptsIndexing
IdentifiedConcepts
IndexedIdentifiedConcepts
andText
Content
RecommenderSystem
Pdf Report
PowerpointPresentation
Image withText
YoutubeVideo
Σw*UA*DC
accessed
rated
commented
downloadedEducationEducational psychologyKnowledgeLearningKnowledge ManagementHuman-Computer InteractionInterdisciplinarityAcademiaSystems thinkingScientific methodEducational technologyVirtual learning environment
User
Identified Concepts (DC)
Identified User Concepts(UC)
Tracked Activities (UA)
EducationEducational psychology
KnowledgeLearning
Knowledge ManagementSystems thinkingScientific method
Educational technologyVirtual learning environment
LearningKnowledge Management
Human-Computer InteractionInterdisciplinarity
EducationEducational psychology
Academia
Building Interests Profile
Providing Recommendations
Step 2. Use vector cosine similarity for scoring and ranking
Step 1. Compute TF-IDF for each term in the vectors
Step 0. Represent each content item concepts using the document vector model
Implementation & Evaluation
Graasp
AlchemyAPI for concept extractionActivityStreams / xAPI for Interaction Tracking
ElasticSearch for storage and recommendations
Open-source tools for text extraction
Implementation in Graasp
Preliminary Evaluation• Six pre-service teachers, participants of a workshop on
inquiry-based learning
• They were newly registered users (no interaction data)
• Interacted for 2 hours
• Survey from three parts
1. General disposition towards the interests identification and the interests-based recommender
2. System Usability Scale for the solution
3. Recommender Precision
Evaluation Outcomes
Complete results: https://goo.gl/Wes6uP
Would like to edit interests
Concerned about privacy
We use a 5-point Likert Scale
Evaluation Outcomes
Was easy to use
Easy to get started
Evaluation Outcomes
Misidentified concepts in popular content can push up irrelevant concepts
Two groups: relevant and irrelevant interests
Two groups: relevant and irrelevant suggestions
Conclusions• Proposed a general and scalable approach
deployable in systems where content and interactions are available
• Allows users to modify the interests
• Implementation in a real system, can be used as a guideline
• Preliminary evaluation in an authentic setting
Future Work• Address misidentified concepts-related issues
• Learn optimal action weights
• Incorporate concept relevance score into similarity
• Substantial Evaluation
• Run a bigger scale evaluation
• Check not only precision, but as well recall
• Compare to existing approaches. Dataset?
