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title: UTS2116 University Today Week 10

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# UTS2116 University Today Week 10

#### Content Creator vs Aggregator

Aggregator -> encourage people to create content themselves. They are more like a combination/hybrid of different fields and there's no niche, there's a wider range of subject. 

Many content creator

Content Creator -> Can be youtuber or professor that creates their own content. Specific subject content expert, produces content for a small subject

Conventional professors, can choose to draw it out from their asynchronous library for years.

> Most biology professors, for instance, would find themselves hard pressed to match the pedagogical quality, production values and inspirational nature of Eric Lander’s online Introduction to Biology course at M.I.T. That free course currently has over 134,000 students enrolled this semester.

Universities don't really like, but only dampen on it. The aggregators threaten the physical campus popularity.

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Maintaining academic integrity in the age of online examinations - exam-proctoring spyware Respondus Monitor
-> A new piece of information

One thing you agree with

One thing you disagree with

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1)	How was the field of educational data science defined in this article?

An emerging field of educational research that deploys new digital methods and algorithmic data analytics technologies alongside psychological theories to measure, know and understand institutions, practices and processes of learning.

represents a nexus of big data analysis and educational research and instantiates a new transdisciplinary professional infrastructure for educational knowledge production and theory generation


Niche field that requires CS and education industry. You need both of them to work together so you can interpret the big data meaningfully. 

Data generation can be analysed to use prediction power to predict student's potential. One perks of it.

Think about how to explain this, what does it mean or do? What are they using (the kind of data), the history of the genealogy of the educational data science

1. What does it mean by educational data science
    - An emerging field of educational research that deploys new digital methods and algorithmic data analytics technologies alongside psychological theories to measure, know and understand institutions, practices and processes of learning.
    - represents a nexus of big data analysis and educational research and instantiates a new transdisciplinary professional infrastructure for educational knowledge production and theory generation

2. What kind of data are they using?
    - Data can be biased. Data doesn't speak for itself, it needs someone to interpret and process it
    - Transparency in how the algorithm being run is lacking due to the privatisation
    - Data is collected
    - log files, conversational records, peer assessments, online search and navigation behaviour

3. History of genealogy
    - Emergence of education data science from 2004–2007
    - Administration records, attendance data
    - Lytics Lab at Stanford
        - On understanding online learners, including dropout prediction tools and analytics of attainment gaps and studies that evaluate ‘digital instruction’, as well as the development of new ‘learning tools’ such as social learning platforms
    - Stanford University Learning Analytics Workgroup
    - SoLAR Institute