Towards The Use of an Automated Scoring Framework at the Medical Council of Canada An Exploratory Approach

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1 Towards The Use of an Automated Scoring Framework at the Medical Council of Canada An Exploratory Approach This document will demonstrate the process required for developing automated scoring models. The process is explained using a sample dataset from the Medical Council of Canada. Syed M. Fahad Latifi 1, Mark J. Gierl 1, and André-Philippe Boulais 2 University of Alberta 1, Medical Council of Canada 2 August, 2013.

2 Table of Contents Executive Summary... ii Section One: Overview of Machine Learning Environment... 1 Loading the Machine Learning Environment... 1 Modules within MLE... 1 Data Transformation Feature Extraction Module... 3 Machine Learning Model Building and Evaluation Module... 4 Automated Scoring Absolute Score Prediction Module... 5 Section Two: Exemplar for Developing the AES Framework... 6 Data Preprocessing for Feature Extraction... 6 Elements of Feature Extraction... 6 Model Building and Validation... 9 Exploring the Prediction Model Automated Score Prediction Summary and Conclusion Further Readings i

3 EXECUTIVE SUMMARY This manual is organized into two main sections. The first section presents the modules within the machine learning environment (MLE) of Light Summarization Integrated Development Environment (LightSIDE), Version The modules required for developing the automated scoring engines are presented using screenshots. Also, the options within each screen are discussed briefly. The second section demonstrates the actual automated scoring process, using exemplar data from clinical decision making constructed response (CDM-CR) question used by the Medical Council of Canada. Data pre-processing, including import, export, and data-conversion from MS Access/ Excel, and then to LightSIDE, are presented. Screenshots are used to describe the elements of the feature extraction, model building and validation, exploring prediction model, and automatic score prediction process. ii

4 SECTION ONE: OVERVIEW OF MACHINE LEARNING ENVIRONMENT In this section, an overview of the machine learning environment called LightSIDE is presented. Necessary modules for developing the automated scoring process are demonstrated. LOADING THE MACHINE LEARNING ENVIRONMENT After launching LightSIDE, a splash screen will appear while the machine learning environment (MLE) of LightSIDE is loading in the background as shown in Figure 1-1. Figure 1-1. LightSIDE splash screen. MODULES WITHIN MLE Once the MLE is loaded, the user will see the following tabs, each one referring to distinct modules (see Figure 1-2). Extract Feature Restructure Data Build Models Explore Results Compare Models Predict Labels 1

5 Figure 1-2.Major module for the automated scoring process in LightSIDE. Three modules are needed to develop the automated scoring process: 1) feature extraction, 2) model building and validation, and, 3) model absolute prediction. Within the LightSIDE environment, these modules are named Extract Feature, Build Models and Predict Labels, respectively, as indicated by circles in Figure

6 DATA TRANSFORMATION FEATURE EXTRACTION MODULE Feature extraction is the process of transforming examinee answers (i.e., text) so that statistical relationships between elements-of-text and human scores can be established. Options within the feature extraction module are numbered / encircled in Figure 1-3 and discussed as follows: 1. CSV files: Used to select the input data set for initiating the feature extraction process 2. Feature Extraction Plugins: Used to choose the method for extracting features from input datasets 3. Configure Basic Feature: Used to configure the feature extraction mechanism 4. Class: Used for specifying the annotated field (score label) within datasets 5. Text Field: Used for specifying the text field containing written answers, within datasets 6. Name: Used to name the list / table of extracted features 7. Rare Threshold: Used to set the minimum frequency for proposed feature extraction 8. Evaluation Display: Used to list/display feature evaluation-indices 9. Extract: Button used for initiating the feature extraction process 10. Feature in Table: Lists / Display extracted features which are exportable as CSV Figure 1-3. Various options while configuring the feature extraction process. 3

7 MACHINE LEARNING MODEL BUILDING AND EVALUATION MODULE Outcomes from the feature extraction process (the training datasets) are then employed to build and evaluate the scoring models. The machine learning process is employed for building and evaluating the score prediction models. The options within the model building and evaluation process are shown in Figure 1-4 and discussed as follows: 1. Feature Tables: Shows the list of available feature tables 2. Learning Plugin: Used to select and configure the learning algorithms 3. Cross-Validation: Used for defining the method for evaluating scoring models 4. Name: Used for defining the name of scoring model 5. Train: This button is used for initiating the training process 6. Trained Model: Lists the name of models built 7. Model Evaluation Matrix: Shows the expected performance indices of the scoring model 8. Model Confusion Matrix: Confusion matrix from model building process will be shown Figure 1-4. Elements of the model building process. 4

8 AUTOMATED SCORING ABSOLUTE SCORE PREDICTION MODULE Scoring models are built and evaluated using the machine learning module. Once created, the models can then be applied to a new / separate pool of responses using the score prediction module. Options within the score prediction module are highlighted in Figure Model to Apply: Lists the scoring models, which could be used for the automated scoring process 2. Unlabeled Data: Used to upload new dataset (in CSV format) for automated score prediction 3. New Column Name: Used to name the column containing predicted score label 4. Predict: This button will initiate the automated score prediction process 5. Score Column: Column with machine predicted scores is appended beside text column 6. Export: This button will export results from grid to CSV file for further processing Figure 1-5. Elements of the score prediction process. 5

9 SECTION TWO: EXEMPLAR FOR DEVELOPING THE AES FRAMEWORK In this section we demonstrate the logic that underlies the score prediction workflow using an exemplar dataset based on Medical Council of Canada clinical decision-making constructed response (CDM-CR) questions. For this demonstration, question (i.e. Problem no. 165, Question no. 1, Case no. 1 and Key Feature 1) is used as the training dataset. For this example, 1266 responses from the spring 2011 and 2012 administration were used as training dataset. DATA PREPROCESSING FOR FEATURE EXTRACTION Raw scores from markers must be annotated into labels. That is, each score point is replaced by text scoring labels. For instance, if an essay is scored 4 out of 5, then the annotated score will be four (a text-label for number 4). This annotation is a requirement for the machine learning (classification) process. Further, the input dataset must also be cleaned from answer separators (the pipeline- symbols currently used by the MCC dataset extraction procedure). Once the raw scores are annotated and separators are removed from examinees responses, the dataset must be converted into a CSV file format. The layout of CSV file is shown in Figure 2-1. Figure 2-1. Layout of the input file in CSV format for feature extraction. ELEMENTS OF FEATURE EXTRACTION The next step is the configuration of the feature extraction module, shown in Figure 2-2. First, a CSV file containing training responses is selected using the CSV files option. Then the extraction plugins is 6

10 chosen using the Configure Basic Feature option. The Class and Text Field options are used to flag appropriate columns within the CSV file (i.e., the training dataset). Also, the rare threshold is set to five 1, meaning that the feature should only be considered if it appears at least five times in the training dataset. Figure 2-2. An exemplar for the feature extraction process using responses from a CDM-CR question. After configuring the feature extraction module, the process is initiated by pressing the Extract button. This process will objectively transform the training dataset by computing indices (recall, precision, etc.) corresponding to each feature. The values of these indices are summarized by selecting checkboxes in Basic Table Statistics, also encircled in Figure 2-2. For this example, there were 209 features extracted from the training dataset. As shown in Figure 2-3, the extracted features are exportable into a CSV file. For this example, the list of extracted features is named/saved as 1 All configurations shown in Figure 2-2 are tested to produce optimum results for overall score prediction process using our sample MCC dataset. 7

11 Features_ Extracted features are then used for model building and the evaluation process, which are presented next. Figure 2-3. List of extracted features from the exemplar dataset. 8

12 MODEL BUILDING AND VALIDATION To train and build the automated scoring prediction model, the machine learning algorithm (MLA) is used. Among the large suite of available MLAs, Sequential Minimum Optimization (SMO) is selected for our demonstration. The process starts by importing extracted features for training and building the score prediction model. The MLA builds and refine the model iteratively using a cross-folding validation process. Figure 2-4. An exemplar for the model building and validation process. Training the MLA starts by selecting the feature table, as shown in Figure 2-4, the table named Features_ is passed on to the MLA (SMO in this case). Further, the cross-validation is achieved by means of data-folding. Data-folding is a process of splitting the training dataset into n subsets, randomly. The initial score prediction model is built using an arbitrary subset of the training dataset. The model is then refined (build/rebuilds) using subsequent n-1 subsets. Researchers who use this method have found that splitting the training dataset into ten subsets (n= 10) often results in a convergence to the optimum score prediction model (we have also tested it for CDM-CR responses and findings are consistent). Hence, the same method was followed in this example. 9

13 Figure 2-5. Model evaluation and expected prediction power. As shown in Figure 2-5, three major outcomes are reported as part of model building and validation process. The outcomes include an analysis of the model expected prediction power, consistency matrix, and confusion matrix. For this example, it is expected that the model would correctly predict 97.5% (accuracy = 0.975) of the scores, and the (human-machine) agreement, beyond chance alone, would be (Kappa = 0.914). The confusion matrix summarizes the dispersion during the model building process; it also highlights true-negative and true-positive. The final model is saved with the name of SMO_

14 EXPLORING THE PREDICTION MODEL Features and association of features with the scoring rubric (score label) constitutes the development of score prediction model. The analysis of these constituents and their impact in the overall prediction process is usually referred to as error analysis. The Explore results option provides this facility.. Figure 2-6. Elements of Explore results option The list of features is shown in Figure 2-6. For example, the feature insomnia appeared 45 times among the 212 CDM-CR responses, predicted as a score of zero. The average value of this feature is shown as (i.e. 45 / 212), suggesting that the feature insomnia appears 21% of the timein the predicted, zero-scored, CDM-CR response dataset. The details about insomnia could be seen in the bottom part of the Explore Results option screen. Basic error analysis using Explore results is presented in Figure 2-7. The feature generalized occurred three times in an incorrectly predicted CDM-CR response dataset. The average value is computed as 0.25 ( 3/12), meaning that from total of 12 incorrectly predicted dataset, 25% contains feature named generalized. Feature generalized could then further evaluated using the Evaluations to Display option. 11

15 Figure 2-7. Basic error analysis using Explore Results Notice that the Exploration plugin is set to Document display, meaning that the actual response could be investigated in the bottom part of the Explore Results screen. 12

16 AUTOMATED SCORE PREDICTION The final score prediction model is then employed for the automated score prediction process. As shown in Figure 2-8, 856 responses to question are imported using the Unlabeled Data (CSV) menu. Then the saved model, SMO_ , is selected and the column name is defined (Predicted_Score), which would contain the predicted score. The Predict button initiates the automated scoring process. Figure 2-8. Elements of the automated scoring module. As presented in Figure 2-9, the machine predicted score is populated along with the examinees actual responses. The predicted score together with the examinees actual response could then be exported to a CSV file additional analysis or for operational use. 13

17 Figure 2-9. Machine predicted scoring label. The automated score could then be compared against marks from Round-I (the Physician scores) and only the flagged discrepant cases would be routed back to the Physicians for additional scoring and analysis. 14

18 SUMMARY AND CONCLUSION In this manual, the process for implementing an automated scoring framework, using the open-source machine learning environment of LightSIDE, is demonstrated. The score prediction model was build using exemplar data from the CDM-CR question. We found that the automated mechanism for scoring the exemplar dataset is quite accurate. Automated scoring of CDM-CR questions was found to be feasible and in high agreement with physician-based scores. The process described in this manual could be used to develop computer-assisted scoring procedure/program at the MCC, where each written response would be marked by physicians and computer, and second round physicians judgment will only be sought if there is a discrepancy between the computer and physician scores. In sum, the scoring framework we demonstrated provides strong evidence to support the use of automated scoring in a high-stakes medical licensure testing context. 15

19 FURTHER READINGS Attali, Y. (2013), Validity and Reliability of Automated Essay Scoring. In M.D. Shermis& J.C. Burstein (Eds.), Handbook of Automated Essay Evaluation: current application and new directions (pp ). New York: Psychology Press. Brew, C., &Leacocle, C. (2013). Automated Short Answer Scoring. In M.D. Shermis& J.C. Burstein (Eds.), Handbook of Automated Essay Evaluation: current application and new directions (pp ). New York: Psychology Press. Dikli, S. (2006).An overview of automated scoring of essays. The Journal of Technology, Learning and Assessment, 5(1). Fürnkranz, J. (1998). A study using n-gram features for text categorization. Austrian Research Institute for Artificial Intelligence, 3(1998), Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), Mayfield, E., & Rose, C. P. (2013), LightSIDE: Open Source Machine Learning for Text. In M.D. Shermis& J.C. Burstein (Eds.), Handbook of Automated Essay Evaluation: current application and new directions (pp ). New York: Psychology Press. Mayfield, E., & Rose, C. P. (2012).LightSIDE Text Mining and Machine Learning User s Manual. Retrieved from Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, CiteSeerX: Shermis, M. D. (2010). Automated essay scoring in a high stakes testing environment.in Innovative Assessment for the 21st Century (pp ).Springer US. Shermis, M.D., &Hamner, B. (2012).Contrasting state-of-the-art automated scoring of essays: analysis. Paper presented at the National Council on Measurement in Education, Vancouver, BC, Canada. Viera, A. J., & Garrett, J. M. (2005). Understanding inter-observer agreement: the kappa statistic. Family Medicine, 37(5), Williamson, D. M. (2009, April). A framework for implementing automated scoring. In Annual Meeting of the American Educational Research Association and the National Council on Measurement in Education, San Diego, CA. 16

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