5 research outputs found
Target Sequence Clustering
No full textResearchers have discovered many successful algorithms and methodologies for solving problems at the intersection of machine learning and education research. This umbrella category, “educational data mining,” has enjoyed a series of successes that span the research process, from post-hoc data analysis that generates models to the use of those models in successful educational interventions. However, most of these successes have arisen from the use of pre-existing psychological and educational constructs (e.g., guessing) and thus from the use of semi-supervised or fully-supervised machine learning algorithms. Algorithms for novel discovery, also known as unsupervised clustering, have enjoyed significantly fewer successes in this domain, partially because education data exhibit unique, complex structure.
This thesis is a mixture of algorithm development, simulation, and experimentation on real-world data, all designed to define and test a novel paradigm for clustering in education (and a range of other domains). This paradigm, target clustering, revolves around the inclusion of high-level targets, such as student learning from pre-test to post-test. This approach differs from other existing machine learning approaches in that it is designed completely, from the initial concept to the final execution, for solving educational research problems, taking advantage of the structural complexities that are problematic for other algorithms. This thesis includes a range of data sets drawn from a variety of research domains, but does not include new data from experiments in the psychological sense.1 However, the thesis includes analysis of methodology, results, and implications from an educational research perspective and relies entirely on education data and research problems.</p
The 'white worrier' in South Australia: Attitudes to multiculturalism, immigration and reconciliation
No full textIn his analysis of ‘paranoid nationalism’, Hage (2003: xii, 2) coins the figure of the ‘white worrier’ to identify how white Australians marginalized by the inequalities of economic rationalism and globalization displace their anxieties onto even weaker ‘others’, Aboriginal people and migrants, particularly refugees. Hage’s ideas are applied to the discourses used by young South Australians when they discuss Australian multiculturalism, immigration and reconciliation. Hage’s suggestion that white worrying is the response of the white working class male to his economic and ideological marginalization is only partially supported in this sample of young people. While those from non- English speaking and Indigenous backgrounds are much less likely to be ‘paranoid nationalists’, fear and loathing of the other are expressed across the socio-economic spectrum of young ‘white’ Australians, with exposure to a university education, either on the part of respondents or their parents, being the main antidote to hostile attitudes to the ‘other’.Chilla Bulbec
A Response Time Model for Bottom-Out Hints as Worked Examples
No full textStudents can use an educational system's help in unexpected ways. For example, they may bypass abstract hints in search of a concrete solution. This behavior has traditionally been labeled as a form of gaming or help abuse. We propose that some examples of this behavior are not abusive and that bottom-out hints can act as worked examples. We create a model for distinguishing good student use of bottom-out hints from bad student use of bottom-out hints by means of logged response times. We show that this model not only predicts learning, but captures behaviors related to self-explanation
Optimizing Student Models for Causality
No full textComplex student models often include key parameters critical to their behavior and effectiveness. For example, one meta-cognitive model of student help-seeking in intelligent tutors includes 15 rules and 10 parameters. We explore
whether or not this model can be improved both in accuracy and generalization by using a variety of techniques to select and tune parameters.We show that such techniques are important by demonstrating that the normal method of fitting parameters on an initial data set generalizes poorly to new test data sets. We then show that
stepwise regression can improve generalization, but at a cost to initial performance. Finally, we show that causal search algorithms can yield simpler models that perform comparably on test data, but without the loss in training set performance. The resulting help-seeking model is easier to understand and classifies a more realistic number of student actions as help-seeking errors
Fig. 12 in Two New Species of Frogs of the Genus Colostethus (Dendrobatidae) from Peru and a Redescription of C. trilineatus (Boulenger, 1883)
No full textFig. 12. Narrow band (59 Hz) spectrogram and waveform of advertisement call of Colostethus juaniiPublished as part of GRANT, TARAN & RODRÍGUEZ, LILY O., 2001, Two New Species of Frogs of the Genus Colostethus (Dendrobatidae) from Peru and a Redescription of C. trilineatus (Boulenger, 1883), pp. 1-24 in American Museum Novitates 3355 on page 17, DOI: 10.1206/0003-0082(2001)3552.0.CO;2, http://zenodo.org/record/537234
