Dataset Information

Visual Literacy in Bloom: Using Bloom's Taxonomy to Support Visual Learning Skills.

ABSTRACT: Vision and Change identifies science communication as one of the core competencies in undergraduate biology. Visual representations are an integral part of science communication, allowing ideas to be shared among and between scientists and the public. As such, development of scientific visual literacy should be a desired outcome of undergraduate instruction. We developed the Visualization Blooming Tool (VBT), an adaptation of Bloom's taxonomy specifically focused on visual representations, to aid instructors in designing instruction and assessments to target scientific visual literacy in undergraduate instruction. In this article, we identify the need for the VBT, describe its development, and provide concrete examples of its application to a curriculum redesign effort in undergraduate biochemistry.

SUBMITTER: Arneson JB

PROVIDER: S-EPMC6007771 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Visual Literacy in Bloom: Using Bloom's Taxonomy to Support Visual Learning Skills.

Arneson Jessie B JB Offerdahl Erika G EG

CBE life sciences education 20180101 1

<i>Vision and Change</i> identifies science communication as one of the core competencies in undergraduate biology. Visual representations are an integral part of science communication, allowing ideas to be shared among and between scientists and the public. As such, development of scientific visual literacy should be a desired outcome of undergraduate instruction. We developed the Visualization Blooming Tool (VBT), an adaptation of Bloom's taxonomy specifically focused on visual representations ...[more]

PMID: 29351910

Similar Datasets

Project description:The assessment of examination questions is crucial in educational institutes since examination is one of the most common methods to evaluate students' achievement in specific course. Therefore, there is a crucial need to construct a balanced and high-quality exam, which satisfies different cognitive levels. Thus, many lecturers rely on Bloom's taxonomy cognitive domain, which is a popular framework developed for the purpose of assessing students' intellectual abilities and skills. Several works have been proposed to automatically handle the classification of questions in accordance with Bloom's taxonomy. Most of these works classify questions according to specific domain. As a result, there is a lack of technique of classifying questions that belong to the multi-domain areas. The aim of this paper is to present a classification model to classify exam questions based on Bloom's taxonomy that belong to several areas. This study proposes a method for classifying questions automatically, by extracting two features, TFPOS-IDF and word2vec. The purpose of the first feature was to calculate the term frequency-inverse document frequency based on part of speech, in order to assign a suitable weight for essential words in the question. The second feature, pre-trained word2vec, was used to boost the classification process. Then, the combination of these features was fed into three different classifiers; K-Nearest Neighbour, Logistic Regression, and Support Vector Machine, in order to classify the questions. The experiments used two datasets. The first dataset contained 141 questions, while the other dataset contained 600 questions. The classification result for the first dataset achieved an average of 71.1%, 82.3% and 83.7% weighted F1-measure respectively. The classification result for the second dataset achieved an average of 85.4%, 89.4% and 89.7% weighted F1-measure respectively. The finding from this study showed that the proposed method is significant in classifying questions from multiple domains based on Bloom's taxonomy.

Project description:BACKGROUND: Internal medicine residents must be competent in advanced cardiac life support (ACLS) for board certification. OBJECTIVE: To use a medical simulator to assess postgraduate year 2 (PGY-2) residents' baseline proficiency in ACLS scenarios and evaluate the impact of an educational intervention grounded in deliberate practice on skill development to mastery standards. DESIGN: Pretest-posttest design without control group. After baseline evaluation, residents received 4, 2-hour ACLS education sessions using a medical simulator. Residents were then retested. Residents who did not achieve a research-derived minimum passing score (MPS) on each ACLS problem had more deliberate practice and were retested until the MPS was reached. PARTICIPANTS: Forty-one PGY-2 internal medicine residents in a university-affiliated program. MEASUREMENTS: Observational checklists based on American Heart Association (AHA) guidelines with interrater and internal consistency reliability estimates; deliberate practice time needed for residents to achieve minimum competency standards; demographics; United States Medical Licensing Examination Step 1 and Step 2 scores; and resident ratings of program quality and utility. RESULTS: Performance improved significantly after simulator training. All residents met or exceeded the mastery competency standard. The amount of practice time needed to reach the MPS was a powerful (negative) predictor of posttest performance. The education program was rated highly. CONCLUSIONS: A curriculum featuring deliberate practice dramatically increased the skills of residents in ACLS scenarios. Residents needed different amounts of training time to achieve minimum competency standards. Residents enjoy training, evaluation, and feedback in a simulated clinical environment. This mastery learning program and other competency-based efforts illustrate outcome-based medical education that is now prominent in accreditation reform of residency education.

Dataset Information

Visual Literacy in Bloom: Using Bloom's Taxonomy to Support Visual Learning Skills.

Publications

Visual Literacy in Bloom: Using Bloom's Taxonomy to Support Visual Learning Skills.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets