Dataset Information

Optimizing learning of scientific category knowledge in the classroom: the case of plant identification.

ABSTRACT: Learning to identify organisms is extraordinarily difficult, yet trained field biologists can quickly and easily identify organisms at a glance. They do this without recourse to the use of traditional characters or identification devices. Achieving this type of recognition accuracy is a goal of many courses in plant systematics. Teaching plant identification is difficult because of variability in the plants' appearance, the difficulty of bringing them into the classroom, and the difficulty of taking students into the field. To solve these problems, we developed and tested a cognitive psychology-based computer program to teach plant identification. The program incorporates presentation of plant images in a homework-based, active-learning format that was developed to stimulate expert-level visual recognition. A controlled experimental test using a within-subject design was performed against traditional study methods in the context of a college course in plant systematics. Use of the program resulted in an 8-25% statistically significant improvement in final exam scores, depending on the type of identification question used (living plants, photographs, written descriptions). The software demonstrates how the use of routines to train perceptual expertise, interleaved examples, spaced repetition, and retrieval practice can be used to train identification of complex and highly variable objects.

SUBMITTER: Kirchoff BK

PROVIDER: S-EPMC4152204 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Optimizing learning of scientific category knowledge in the classroom: the case of plant identification.

Kirchoff Bruce K BK Delaney Peter F PF Horton Meg M Dellinger-Johnston Rebecca R

CBE life sciences education 20140101 3

Learning to identify organisms is extraordinarily difficult, yet trained field biologists can quickly and easily identify organisms at a glance. They do this without recourse to the use of traditional characters or identification devices. Achieving this type of recognition accuracy is a goal of many courses in plant systematics. Teaching plant identification is difficult because of variability in the plants' appearance, the difficulty of bringing them into the classroom, and the difficulty of ta ...[more]

PMID: 25185226

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Project description:BACKGROUND:Text mining (TM) methods have been used extensively to extract relations and events from the literature. In addition, TM techniques have been used to extract various types or dimensions of interpretative information, known as Meta-Knowledge (MK), from the context of relations and events, e.g. negation, speculation, certainty and knowledge type. However, most existing methods have focussed on the extraction of individual dimensions of MK, without investigating how they can be combined to obtain even richer contextual information. In this paper, we describe a novel, supervised method to extract new MK dimensions that encode Research Hypotheses (an author's intended knowledge gain) and New Knowledge (an author's findings). The method incorporates various features, including a combination of simple MK dimensions. METHODS:We identify previously explored dimensions and then use a random forest to combine these with linguistic features into a classification model. To facilitate evaluation of the model, we have enriched two existing corpora annotated with relations and events, i.e., a subset of the GENIA-MK corpus and the EU-ADR corpus, by adding attributes to encode whether each relation or event corresponds to Research Hypothesis or New Knowledge. In the GENIA-MK corpus, these new attributes complement simpler MK dimensions that had previously been annotated. RESULTS:We show that our approach is able to assign different types of MK dimensions to relations and events with a high degree of accuracy. Firstly, our method is able to improve upon the previously reported state of the art performance for an existing dimension, i.e., Knowledge Type. Secondly, we also demonstrate high F1-score in predicting the new dimensions of Research Hypothesis (GENIA: 0.914, EU-ADR 0.802) and New Knowledge (GENIA: 0.829, EU-ADR 0.836). CONCLUSION:We have presented a novel approach for predicting New Knowledge and Research Hypothesis, which combines simple MK dimensions to achieve high F1-scores. The extraction of such information is valuable for a number of practical TM applications.

Dataset Information

Optimizing learning of scientific category knowledge in the classroom: the case of plant identification.

Publications

Optimizing learning of scientific category knowledge in the classroom: the case of plant identification.

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