Edited by Steve Bamlett, Tuesday, 14 Jun 2016, 14:26
A.Comment on
the method of partial reading of a paper recommended in the exercise
instructions.
B.How does
this paper contribute to my learning about ‘learning analytics?
A.The recommendations for reading are sensible and coherent, especially when
reading papers in volume. First the abstract, then findings (especially
graphical & tabular representations thereof, then the practical
implications (a final test of relevance to your own research purpose) and, only
then, catching up on the research background / literature review material. One
function of this method is to test whether the paper will yield any data that
matters to you – reading can be abandoned early if found irrelevant, wasting
less time and perhaps the trouble, cost (to self and planet) of printing the
paper.
In this context, however, I found the method less useful –
as with other exercises it led to the risk of reading with a funnel vision,
where precisely what I (personally at least) need as a learner is to situate LA
in wider contexts of academic and professional development fields. It is issues
about the latter that I tended to pick out therefore.
B.I found two things of interest:
a.First this paper was the first I have read to emphasize (231) the ‘messiness’
of big data in comparison to the attempts to sharpen the edges associated with
classical (especially quantitative) research method. Together with this goes a
great deal of concern for the limitations of such data (without denying its
obvious strengths). These strengths and potential usages tend, as expressed in
this paper (232ff) to present LA as a means of improving the granularity of
descriptions of a current state of affairs (238) rather than being predictive.
Their predictive quality is totalized mainly in suggesting hypotheses for more rigorous
testing.
b.Second, it contextualized LA in terms of the SOURCES of the research it
fostered. In education, it tended to emerge in hybrid forms and was subsumed
within guiding methodologies – often qualitative – whilst where computer
sciences where dominant, there was much more openness to simple quantitative
reporting (sometimes without a guiding methodology). This view could be
dominant in LAK & SoLAR events (238) because of the predominance therein of
computer science.
C.Hence it is interesting to consider the differences in citation between LAK
and Google Scholar citations. Whereas the latter may be the home of generic
scholarship searches, the former is specialized. Hence LAK citations show no
items causing preponderant interest in them. Thus the interest in a specialist Social
network analysis text is middling. In Google scholar the number of citations
for that same text vastly outweigh interest in other articles. The total range in
number of citations is 16680 – 24 = 16656, the range between the SNA text and
the next most popular is 16680 – 439 = 16241, hardly less. It is almost as if
that text (Wasserman & Faust 1994) was the only one of significant interest
in that wider research-led community.
D.I think therefore one needs to approach LA cautiously and its rhetorical
hype more so.
Dawson, S., Gašević, D., Siemens, G., Joksimovic, S.
(2014) ‘Current State and Future Trends: A citation Network Analysis of the
Learning Analytics Field’ in LAK ’14 231
– 240.
Wasserman, S. & Faust, K. (1994) Social Network Analysis: Methods and Applications. New York,
Cambridge University Press.
Citation Networks Activity 15 Block 4 Dawson et. al. (2014)
A. Comment on the method of partial reading of a paper recommended in the exercise instructions.
B. How does this paper contribute to my learning about ‘learning analytics?
A. The recommendations for reading are sensible and coherent, especially when reading papers in volume. First the abstract, then findings (especially graphical & tabular representations thereof, then the practical implications (a final test of relevance to your own research purpose) and, only then, catching up on the research background / literature review material. One function of this method is to test whether the paper will yield any data that matters to you – reading can be abandoned early if found irrelevant, wasting less time and perhaps the trouble, cost (to self and planet) of printing the paper.
In this context, however, I found the method less useful – as with other exercises it led to the risk of reading with a funnel vision, where precisely what I (personally at least) need as a learner is to situate LA in wider contexts of academic and professional development fields. It is issues about the latter that I tended to pick out therefore.
B. I found two things of interest:
a. First this paper was the first I have read to emphasize (231) the ‘messiness’ of big data in comparison to the attempts to sharpen the edges associated with classical (especially quantitative) research method. Together with this goes a great deal of concern for the limitations of such data (without denying its obvious strengths). These strengths and potential usages tend, as expressed in this paper (232ff) to present LA as a means of improving the granularity of descriptions of a current state of affairs (238) rather than being predictive. Their predictive quality is totalized mainly in suggesting hypotheses for more rigorous testing.
b. Second, it contextualized LA in terms of the SOURCES of the research it fostered. In education, it tended to emerge in hybrid forms and was subsumed within guiding methodologies – often qualitative – whilst where computer sciences where dominant, there was much more openness to simple quantitative reporting (sometimes without a guiding methodology). This view could be dominant in LAK & SoLAR events (238) because of the predominance therein of computer science.
C. Hence it is interesting to consider the differences in citation between LAK and Google Scholar citations. Whereas the latter may be the home of generic scholarship searches, the former is specialized. Hence LAK citations show no items causing preponderant interest in them. Thus the interest in a specialist Social network analysis text is middling. In Google scholar the number of citations for that same text vastly outweigh interest in other articles. The total range in number of citations is 16680 – 24 = 16656, the range between the SNA text and the next most popular is 16680 – 439 = 16241, hardly less. It is almost as if that text (Wasserman & Faust 1994) was the only one of significant interest in that wider research-led community.
D. I think therefore one needs to approach LA cautiously and its rhetorical hype more so.
Dawson, S., Gašević, D., Siemens, G., Joksimovic, S. (2014) ‘Current State and Future Trends: A citation Network Analysis of the Learning Analytics Field’ in LAK ’14 231 – 240.
Wasserman, S. & Faust, K. (1994) Social Network Analysis: Methods and Applications. New York, Cambridge University Press.