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Curriculum-Based Measurement progress data: Effects of graph pattern on ease of interpretation.

ABSTRACT: Curriculum-Based Measurement (CBM) is a system for monitoring the progress of and evaluating instructional program effectiveness for students with learning difficulties. Although a large amount of research has been conducted on CBM, little has focused on the interpretation and use of the data for instructional decision-making, despite the fact that it is data use that leads to performance gains. In this study, we examine factors affecting the interpretation of CBM data. Specifically, we examine the effect of CBM graph patterns on ease of graph interpretation. Thirty college/university students completed a two-part study in which they viewed various slope-to-goal and slope-to-slope patterns of CBM-graphed data and answered decision-making questions. Response times and accuracy were measured. Results revealed that graph patterns differed in terms of ease of interpretation. Differences depended on the type of question. Implications for interpretation and use of CBM data for decision-making are discussed.

SUBMITTER: Espin CA

PROVIDER: S-EPMC6428331 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Curriculum-Based Measurement progress data: Effects of graph pattern on ease of interpretation.

Espin Christine A CA Saab Nadira N Pat-El Ron R Boender Priscilla D M PDM van der Veen Joost J

Zeitschrift fur Erziehungswissenschaft : ZfE 20180702 4

Curriculum-Based Measurement (CBM) is a system for monitoring the progress of and evaluating instructional program effectiveness for students with learning difficulties. Although a large amount of research has been conducted on CBM, little has focused on the interpretation and use of the data for instructional decision-making, despite the fact that it is data use that leads to performance gains. In this study, we examine factors affecting the interpretation of CBM data. Specifically, we examine ...[more]

PMID: 30956566

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Project description:BackgroundAdvances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps.ResultsNormalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Å resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Å) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of 0.19 Å root mean squared deviation. Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions.ConclusionsThe fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification and structure segmentation methods. This work may be generalized to transform any 3D grid-based data format into non-linear, continuous, and differentiable format for downstream geometric deep learning applications.

Project description:IntroductionMedical students often do not value attending in-person large group sessions. It is also not clear from prior research whether attendance at large group sessions impact on performance in medical school. The goal of this study was to assess the relationship between voluntary attendance in large group sessions organized as a "flipped classroom" in a new innovative curriculum and students' mastery of clinical applications of basic science knowledge.MethodologyOur students' ability to apply basic science knowledge to clinical problems is assessed via progress testing using three methodologies: a locally developed multiple-choice examination, written examination developed through the National Board of Medical Examiners (NBME) Customized Assessment Services Program and post encounter questions included in a clinical skills examination. We analyzed the relationship between voluntary attendance at weekly large group "flipped classroom" sessions and the students' performance on examinations given at four intervals over the initial 24-week module of the medical school curriculum.ResultsComplete data were available for 167 students. A total of 82 students (49.1%) attended all large group sessions, 65 students (38.9%) missed one or two sessions and 20 students (12.0%) missed three or more sessions. There were no difference between the students in the groups on their medical admission (MCAT) examination scores. The growth in performance from each time point until the next was statistically significant. There was no statistically significant difference in growth between the students who had no absences and those who had one or two absences. Students who missed three or more sessions performed significantly lower than their peers over the 24 week module and were more likely to score one or more standard deviations below the class mean on the assessments.ConclusionsWe found no relationship between attendance and MCAT scores suggesting the differences in performance on the progress tests was not due to initial differences in knowledge or reasoning skills. While the study was not experimental, it suggests large group sessions using a "flipped classroom" approach to provide reinforcement, feedback and practice may be effective for increasing learning and retention in the application of basic science knowledge among first year medical students.

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Curriculum-Based Measurement progress data: Effects of graph pattern on ease of interpretation.

Publications

Curriculum-Based Measurement progress data: Effects of graph pattern on ease of interpretation.

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