Information Technology Reference
In-Depth Information
technology determine our research practices nor can our research practices deter-
mine technologies; they are mutually coconstructed. This chapter presents just a few
possible interpretations that have emerged from our own learning technology inno-
vation and research practices as well as our engagement with research innovations
in other disciplinary domains.
Research technologies and practices are created at and shaped by visions and
actions at several levels. On the highest technological level a symbiosis of Grid and
Web 2.0 computing could enable more integrated and collaborative research prac-
tices and new data-rich research methodologies. Growing attention of educational
research community to social technologies might indicate that learning technol-
ogy researchers will perhaps in the near future embrace these technologies in their
research routines. While they offer great potential to democratize research and make
it more transparent, other methodological advancements and innovations require
more coordinated actions and more targeted solutions to technological challenges.
For example, while our outlined e-inquiry platform could be seen as a bottom-up
emerging social and technological space, its real potential rests on semantically well
linked and integrated components. It would be hard to expect that such integration
could be achieved without a priori coordination and agreement at least on some
major technological specifications, such as for provenance and metadata.
There are many methodological issues in learning technology research that
pertain to the limits of human abilities to process data without research instru-
ments and currently provide a serious challenge for further research advancement.
We are already recording much more data in learning research than are analyzed
and reported; these data deluge offer great opportunity to investigate learning
phenomenon on larger scales and greater levels of detail, but constitutes also
a big methodological challenge (Borgman et al., 2008). We discussed just two
methodological approaches—process analysis and multimedia data—where impor-
tant advancements have been achieved over the last 10 years. This list by no
means complete, with such approaches as educational data mining (Romero &
Ventura, 2007), social network analysis (Wasserman & Faust, 1994), and virtual
ethnographies (Beaulieu, 2004) being rapidly further developed and applied.
We discussed and emphasized some technological issues. In learning technology
research, the phenomenon studied is by nature contextualized and often distributed.
The ways in which the phenomena are studied are often based on interpretations
that are deeply entrenched in our personal experiences. Under such circumstances,
data about the inquiry contexts and how results were produced become an important
part of research output and data. Questions of how to record and share contextual
information are at the core for further advancement in learning technology research.
References
Aalst, W. M. Pv. d, & Günther, C. W. (2007). Finding structure in unstructured processes: The case
of process mining. In T. Basten, G. Juhas & S. Shukla (Eds.),
Proceedings the 7th International
Conference on Applications of Concurrency to System Design (ACSD 2007; Bratislava, Slovak
Republic)
(pp. 3-12). Los Alamitos, CA: IEEE Computer Society Press.
