math and chemistry, 75 issues, k-means clustering algorithm

Subject Index

Downloaded by 80.82.77.83 on December 11, 2017 | http://pubs.acs.org Publication Date (Web): November 20, 2017 | doi: 10.1021/bk-2017-1260.ix002

A Analytics, open source tools Apache tools, sample case study, 44 open sources software tools, 45t data, type, 40 data analysis processes, overview, 41 data analysis, summary of the general process, 42f open source tools, 44 right software for analysis, 43 ATLAS.ti, study of problem solving strategies ATLAS.ti, 140 user interface for ATLAS.ti, overview, 142f data collection and analysis, research method, 142 initial coding, 145f qualitative analysis, 144 using software, data preparation, 143 discussion and conclusions, 150 problem-solving strategy, comparison, 151t limitations, implications, and further research, 152 network views, problem-solving strategies generated, 146 problem-solving, graduate student’s approach, 149f problem-solving, Instructor’s approach, 148f problem-solving, undergraduate student’s approach, 150f problem solving in chemistry, literature review, 134 theoretical framework, 138

C CER, putting the R data and R code presented, 68 functions and programmatic loops, 73 getting the best solution using loops, 81 figure 5A and 5B, reproduction, 84f squares values, histogram of the between group sums, 83f getting what we want with functions, 77 Chem vs Math subscales, 79f code additional visualizations, 80

math and chemistry, 75 issues, k-means clustering algorithm, 76 mach data set, plot, 75f notebooks, transforming documentation, 84 interactive portion, Analysis tab, 85 reproducible and dependable analyses, importance, 86 research notebook produced entirely in R, 87f programming in R, advantages and disadvantages, 67 real life examples, 88 transforming data visualization, 69 Anscombe’s quartet, 69f data visualization, 71 2016 JACS article titles, chord diagram, 72f pre/post outcomes, dynamite plot, 70f what is R?, 67 Comprehensive meta-analysis package CMA, analysis of data, 123 CMA, ANALYSIS TAB, 124f imputed studies, funnel plot, 128f meta-analysis, forest plot, 126f meta-analysis, numerical outcomes, 125f moderator analysis, dialog box, 129f moderator variables, effect sizes, 129f publication bias analysis, funnel plot, 127f sensitivity analysis, 124f STUDIES TAB after all studies, 123f conclusion, 129 Hattie’s work, selected effect sizes, 130t entering data into CMA, 120 CMA, dialog box for entering a moderator, 121f CMA, one of the templates for entering the data, 122f CMA at start, 120f introduction, 117 comprehensive meta-analysis, logic, 119f included studies, discipline, and reported outcomes, 119t meta-analysis, other software solutions, 131 meta-analysis, possible software solutions, 131t

163 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

describe() function, descriptive statistics produced, 102f factor variables, cross tabulation, 102f internal consistency, computing evidence, 104 negative correlation to the scale total, output, 105f introduction, 92 Journal of Chemical Education, data analysis software, 92t psych and lavaan, factor analysis, 106 Bartlett’s and Kaiser-Meyer-Olkin tests, output, 107f eigenvalue results, scree plot, 109f item loadings in factor space, plot, 108f lavaan, fit statistics, 111f lavaan, modification indices provided, 113f lavaan, parameter estimates provided, 112f minipisa items, simplified one-factor CFA model, 110f PCA results, summary, 108f RStudio, installing packages, 93 install.packages, help documentation, 95f likert package, console output, 94f likert package, packages pane, 95f RStudio, installing a package, 94f RStudio pane layout, 94f visualizing response patterns with Likert, 95 default centered bar plot generated with the likert package, 99f environment pane showing pisaitems dataset, 96f filled bar plot generated, 99f heat plot generated with the likert package, 101f Import Dataset menu options, 96f likert package with response distributions, 100f pisaitems dataset, console output, 98f pisaitems dataset, spreadsheet view, 97f pisaitems information, environment pane, 97f user-specified color scheme, 100f

Computer-aided data analysis, introduction book, focus, 3 book, organization, 4


E Electronic learning, crossing boundaries, 21 blended-learning, 22f blended-learning, different views of assessments, 23f conclusion and outlook, 37 Learning Management System Analysis Kit, 26 analysis tool, LMSA Kit, 29 automatic generated criteria-based feedback, 33 default feedback template, 35f estimation process, results, 32f feedback, overall accordance, 36t feedback evaluation, results, 35t LMSA Kit, usage, 28f online chess and student’s abilities, 30 tasks describing one competency, combination, 27f text template editing tool, 34f try to make connections, 23 data mining, ways, 24

L Learning management system beginner researcher, struggles, 10 chemical education research, 11 discussion, 14 D2L survey tool, 15f LMS, quiz function, 16 exploring tools, 12 discussion board, knowledge creation, 13f D2L discussion board, 13f D2L Dropbox folder system, 14f

R R and RStudio, Likert-type survey data analysis, 91 computing descriptive statistics and reliabilities with psych, 102 alpha() function, output, 105f describeBy() function, result, 103f

T Test data in jMetrik, analysis introduction, 49


Nonparametric Characteristic Curves dialog box, 61f nonparametric IRC, 62f scale level analysis and reliability estimates, output, 59f Stereochemistry Concept Inventory, basic item scoring of responses, 54f Test Scaling dialog box, 57f variables tab contains information, 56f jMetrik overview, 50 jMetrik, data analysis, 51f


stereochemistry concept inventory, item 18, 50f jMetrik, analysis of data, 51 advanced item scoring dialog box, 55f create New Database popup window, 52f import Data dialog box, 53f import Data file directory dialog box, 53f Item Analysis dialog box, 58f items q1 and q2, item analysis, 60f jMetrik, starting window, 52f


math and chemistry, 75 issues, k-means clustering algorithm

Recommend Documents