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www.employid.eu
EmployIDEmployID
ChristianVoigt, Barbara Kieslinger,Teresa Schäfer
Zentrum fuer Soziale Innovation,Vienna,...
www.employid.eu
Conceptual Frame
14th July 2017 ChristianVoigt | voigt@zsi.at 2
www.employid.eu
• User acceptance is key for the adoption of a new
technology
• Sentiment analysis is a way to extract fee...
www.employid.eu
• Natural language processing aiming to extract feelings,
affects, emotions or opinions in a text
• Output...
www.employid.eu
• Participants of this course were all employees of public
employment services (Work Coaches)
• Learning o...
www.employid.eu
• Learning analytics is applied to MOOCs (Massive Open Online
Courses
• Text materials (n=1,1170 postings)...
www.employid.eu
Visuals Used
14th July 2017 ChristianVoigt | voigt@zsi.at 7
www.employid.eu
Determinants of positive or negative sentiments
14th July 2017 ChristianVoigt | voigt@zsi.at 8
www.employid.eu
Co-word analysis
14th July 2017 ChristianVoigt | voigt@zsi.at 9
www.employid.eu
Overview on a level of individual learners
14th July 2017 ChristianVoigt | voigt@zsi.at 10
www.employid.eu
Feedback
14th July 2017 ChristianVoigt | voigt@zsi.at 11
www.employid.eu
• “when you express emotions there are greater chances of
learning”
• Linked to overall course evaluation,...
www.employid.eu
• Further use was seen in ‚Managing visibility‘ in MOOCS where
the number of comments can quickly become o...
www.employid.eu
Thanks !
14th July 2017 ChristianVoigt | voigt@zsi.at 14
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User Experiences Around Sentiment Analyses, Facilitating Workplace Learning

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User acceptance is key for the adoption of a new technology. In this work we experiment with a novel service for tutors in workplace learning settings. Sentiment analysis is a way to extract feelings and emotions from a text. In a learning setting such a sentiment analysis can be part of learning analytics. It has the potential to foster the understanding of emotions in shared discussions in learning environments, detect group dynamics as well as the impact of certain topics on learners’ sentiments. However, sentiment analysis presents some challenges too, as lived experiences, expectations and ultimately acceptance of this technology varies greatly and can become barriers to adoption. In order to design a system for learning analytics accepted by tutors we experimented with proof-of-concept prototypes and received valuable feedback from tutors regarding the usefulness of the overall sentiment analysis as well as certain features. The qualitative feedback confirms the overall interest of tutors in sentiment analysis and gives important hints towards more detailed analytical elements.

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User Experiences Around Sentiment Analyses, Facilitating Workplace Learning

  1. 1. www.employid.eu EmployIDEmployID ChristianVoigt, Barbara Kieslinger,Teresa Schäfer Zentrum fuer Soziale Innovation,Vienna,Austria [voigt, kieslinger, schaefer] @zsi.at HCI International 2017 Vancouver, Canada, 9 - 14 July 2017 This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 619619 User Experiences Around Sentiment Analyses, Facilitating Workplace Learning
  2. 2. www.employid.eu Conceptual Frame 14th July 2017 ChristianVoigt | voigt@zsi.at 2
  3. 3. www.employid.eu • User acceptance is key for the adoption of a new technology • Sentiment analysis is a way to extract feelings and emotions from a text • Can be part of learning analytics • Has the potential to foster the understanding of emotions in shared discussions, detect group dynamics as well as the impact of certain topics • However, lived experience of sentiment analysis, related expectations and ultimately acceptance of this technology varies greatly and can become barriers to adoption. Challenge 14th July 2017 ChristianVoigt | voigt@zsi.at 3
  4. 4. www.employid.eu • Natural language processing aiming to extract feelings, affects, emotions or opinions in a text • Outputs can be simple dichotomies (positive versus negative expressions), scoring approaches from −5 (very negative) to +5 (very positive) • Categorical approaches such as distinguishing basic emotions such as joy, trust or anger • Already widely applied in product or movie reviews, political discourse analysis or spam detection Short recap of sentiment analysis 14th July 2017 ChristianVoigt | voigt@zsi.at 4
  5. 5. www.employid.eu • Participants of this course were all employees of public employment services (Work Coaches) • Learning objectives included developing a set of skills and competences in areas like • coaching or • providing labour market information, • the use of digital tools. • Learning for career and labour market transitions occurs across four domains: • relational development; • cognitive development; • practical development; • emotional development The context of workplace learning 14th July 2017 ChristianVoigt | voigt@zsi.at 5
  6. 6. www.employid.eu • Learning analytics is applied to MOOCs (Massive Open Online Courses • Text materials (n=1,1170 postings) from 68 participants • Tutors can not read every posting and monitor the activities of every learner in order to anticipate a conflict or identifying the learners who would need additional support • Number of visualizations (proof-of-concept prototypes) are triggering further visualization • e.g. displaying an overly negative tone in week 2 is enhanced by showing the keywords of the debate, related co-word analyses or relevant text snippets • Five exploratory interviews, presenting the scenarios and prototypes Method 14th July 2017 ChristianVoigt | voigt@zsi.at 6
  7. 7. www.employid.eu Visuals Used 14th July 2017 ChristianVoigt | voigt@zsi.at 7
  8. 8. www.employid.eu Determinants of positive or negative sentiments 14th July 2017 ChristianVoigt | voigt@zsi.at 8
  9. 9. www.employid.eu Co-word analysis 14th July 2017 ChristianVoigt | voigt@zsi.at 9
  10. 10. www.employid.eu Overview on a level of individual learners 14th July 2017 ChristianVoigt | voigt@zsi.at 10
  11. 11. www.employid.eu Feedback 14th July 2017 ChristianVoigt | voigt@zsi.at 11
  12. 12. www.employid.eu • “when you express emotions there are greater chances of learning” • Linked to overall course evaluation, not monitoring • No agreement on whether learners should see the analysis • The fact that categorizations are probabilistic and need to be followed up through ‘in depth’ reading of postings relativized usefulness of sentiment analysis • However, the analysis could support required reporting after the training had been delivered Evaluations and comments … (1/2) 14th July 2017 ChristianVoigt | voigt@zsi.at 12
  13. 13. www.employid.eu • Further use was seen in ‚Managing visibility‘ in MOOCS where the number of comments can quickly become overwhelming (hence comments could be ranked by their emotional content) • Linking emotions to individual learners was perceived as the the least valuable feature • Training the system (i.e. the use of supervised algorithms / machine learning) was not seen as a cost – effective approach, but also because sentiment analysis was not perceived as a core service within their mandate to deliver trainings Evaluations and comments … (2/2) 14th July 2017 ChristianVoigt | voigt@zsi.at 13
  14. 14. www.employid.eu Thanks ! 14th July 2017 ChristianVoigt | voigt@zsi.at 14

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