A successful intervention to improve conceptual knowledge of medical students who rely on memorization of disclosed items

Background: The mere memorization of isolated facts without the claim of integrating them is detrimental to the desired learning outcomes in medical education. The current study was conducted within an environment where items from summative assessments are regularly disclosed by the university and consequently collected into an item bank that is shared between students. Within this environment, we aimed to quantify 1) to which extent students use disclosed items for their preparation for the summative exam, 2) a putative mismatch between (isolated factual) knowledge regarding distinct questions from the item bank and conceptual knowledge, and 3) to which extent this mismatch can be ameliorated by a project aiming to steer student learning away from the memorization of isolated facts toward the acquisition of conceptual knowledge. Methods: This steering project in the midst of the first semester consisted of the implementation of an oral exam based on selected learning objectives, preceded by two seminars. After their summative exam at the end of semester, 135 students performed a voluntary exam for study purposes. Here, authentic (i.e., presumably preknown) physiology questions taken from the item bank were used to assess students' ability to 1) recognize the correct answer in a multiple choice (MC) question, 2) recall the answer (short answer), or 3) display conceptual knowledge closely corresponding to the question presented in the other formats. Additionally, students received a questionnaire addressing their learning habits and attitudes. Results: The median reported percentage of learning time for the summative exam exclusively spent with this item bank was 80%. The results of the voluntary exam indicate that students frequently recognize and recall correct answers of included items without displaying knowledge of the underlying concept. Compared to recall of the correct answer, the probability of giving a correct answer regarding the corresponding basal physiologic concept was lower by 47 percentage points (p <0.001) for topics not included in the steering project. Regarding topics included in the steering project, this discrepancy was reduced to 25.5% (p <0.001). Conclusion: The results of this study demonstrate the influence of disclosed items on student learning and learning outcomes and suggest that a carefully implemented assessment is able to improve conceptual knowledge in physiology.

Two New Models for Item Preknowledge

To evaluate preknowledge detection methods, researchers often conduct simulation studies in which they use models to generate the data. In this article, we propose two new models to represent item preknowledge. Contrary to existing models, we allow the impact of preknowledge to vary across persons and items in order to better represent situations that are encountered in practice. We use three real data sets to evaluate the fit of the new models with respect to two types of preknowledge: items only, and items and the correct answer key. Results show that the two new models provide the best fit compared to several other existing preknowledge models. Furthermore, model parameter estimates were found to vary substantially depending on the type of preknowledge being considered, indicating that answer key disclosure has a profound impact on testing behavior.

Detecting Examinees With Item Preknowledge on Real Data

Recently, Belov & Wollack (2021) developed a method for detecting groups of colluding examinees as cliques in a graph. The objective of this article is to study how the performance of their method on real data with item preknowledge (IP) depends on the mechanism of edge formation governed by a response similarity index (RSI). This study resulted in the development of three new RSIs and demonstrated a remarkable advantage of combining responses and response times for detecting examinees with IP. Possible extensions of this study and recommendations for practitioners were formulated.

The Lack of Robustness of a Statistic Based on the Neyman-Pearson Lemma to Violations of Its Underlying Assumptions

Drasgow, Levine, and Zickar (1996) suggested a statistic based on the Neyman-Pearson lemma (NPL; e.g., Lehmann & Romano, 2005, p. 60) for detecting preknowledge on a known set of items. The statistic is a special case of the optimal appropriateness indices (OAIs) of Levine and Drasgow (1988) and is the most powerful statistic for detecting item preknowledge when the assumptions underlying the statistic hold for the data (e.g., Belov, 2016Belov, 2016; Drasgow et al., 1996). This paper demonstrated using real data analysis that one assumption underlying the statistic of Drasgow et al. (1996) is often likely to be violated in practice. This paper also demonstrated, using simulated data, that the statistic is not robust to realistic violations of its underlying assumptions. Together, the results from the real data and the simulations demonstrate that the statistic of Drasgow et al. (1996) may not always be the optimum statistic in practice and occasionally has smaller power than another statistic for detecting preknowledge on a known set of items, especially when the assumptions underlying the former statistic do not hold. The findings of this paper demonstrate the importance of keeping in mind the assumptions underlying and the limitations of any statistic or method.

Graph Theory Approach to Detect Examinees Involved in Test Collusion

Test collusion (TC) is sharing of test materials or answers to test questions before or during the test (important special case of TC is item preknowledge). Because of potentially large advantages for examinees involved, TC poses a serious threat to the validity of score interpretations. The proposed approach applies graph theory methodology to response similarity analyses for identifying groups of examinees involved in TC without using any knowledge about parts of test that were affected by TC. The approach supports different response similarity indices (specific to a particular type of TC) and different types of groups (connected components, cliques, or near-cliques). A comparison with an up-to-date method using real and simulated data is presented. Possible extensions and practical recommendations are given.

Detecting Examinees With Item Preknowledge in Large-Scale Testing Using Extreme Gradient Boosting (XGBoost)

Researchers frequently use machine-learning methods in many fields. In the area of detecting fraud in testing, there have been relatively few studies that have used these methods to identify potential testing fraud. In this study, a technical review of a recently developed state-of-the-art algorithm, Extreme Gradient Boosting (XGBoost), is provided and the utility of XGBoost in detecting examinees with potential item preknowledge is investigated using a real data set that includes examinees who engaged in fraudulent testing behavior, such as illegally obtaining live test content before the exam. Four different XGBoost models were trained using different sets of input features based on (a) only dichotomous item responses, (b) only nominal item responses, (c) both dichotomous item responses and response times, and (d) both nominal item responses and response times. The predictive performance of each model was evaluated using the area under the receiving operating characteristic curve and several classification measures such as the false-positive rate, true-positive rate, and precision. For comparison purposes, the results from two person-fit statistics on the same data set were also provided. The results indicated that XGBoost successfully classified the honest test takers and fraudulent test takers with item preknowledge. Particularly, the classification performance of XGBoost was reasonably good when the response time information and item responses were both taken into account.

Higher-Order Asymptotics and Its Application to Testing the Equality of the Examinee Ability Over Two Sets of Items

In educational and psychological measurement, researchers and/or practitioners are often interested in examining whether the ability of an examinee is the same over two sets of items. Such problems can arise in measurement of change, detection of cheating on unproctored tests, erasure analysis, detection of item preknowledge, etc. Traditional frequentist approaches that are used in such problems include the Wald test, the likelihood ratio test, and the score test (e.g., Fischer, Appl Psychol Meas 27:3-26, 2003; Finkelman, Weiss, & Kim-Kang, Appl Psychol Meas 34:238-254, 2010; Glas & Dagohoy, Psychometrika 72:159-180, 2007; Guo & Drasgow, Int J Sel Assess 18:351-364, 2010; Klauer & Rettig, Br J Math Stat Psychol 43:193-206, 1990; Sinharay, J Educ Behav Stat 42:46-68, 2017). This paper shows that approaches based on higher-order asymptotics (e.g., Barndorff-Nielsen & Cox, Inference and asymptotics. Springer, London, 1994; Ghosh, Higher order asymptotics. Institute of Mathematical Statistics, Hayward, 1994) can also be used to test for the equality of the examinee ability over two sets of items. The modified signed likelihood ratio test (e.g., Barndorff-Nielsen, Biometrika 73:307-322, 1986) and the Lugannani-Rice approximation (Lugannani & Rice, Adv Appl Prob 12:475-490, 1980), both of which are based on higher-order asymptotics, are shown to provide some improvement over the traditional frequentist approaches in three simulations. Two real data examples are also provided.

Detecting Item Preknowledge Using a Predictive Checking Method

Repeatedly using items in high-stake testing programs provides a chance for test takers to have knowledge of particular items in advance of test administrations. A predictive checking method is proposed to detect whether a person uses preknowledge on repeatedly used items (i.e., possibly compromised items) by using information from secure items that have zero or very low exposure rates. Responses on the secure items are first used to estimate a person's proficiency distribution, and then the corresponding predictive distribution for the person's responses on the possibly compromised items is constructed. The use of preknowledge is identified by comparing the observed responses to the predictive distribution. Different estimation methods for obtaining a person's proficiency distribution and different choices of test statistic in predictive checking are considered. A simulation study was conducted to evaluate the empirical Type I error and power rate of the proposed method. The simulation results suggested that the Type I error of this method is well controlled, and this method is effective in detecting preknowledge when a large proportion of items are compromised even with a short secure section. An empirical example is also presented to demonstrate its practical use.

On the Equivalence of a Likelihood Ratio of Drasgow, Levine, and Zickar (1996) and the Statistic Based on the Neyman-Pearson Lemma of Belov (2016)

Levine and Drasgow (1988) suggested an approach based on the Neyman-Pearson lemma to detect examinees whose response patterns are "aberrant" due to cheating, language issues, and so on. Belov (2016) used the approach of Levine and Drasgow (1988) to suggest a statistic based on the Neyman-Pearson Lemma (SBNPL) to detect item preknowledge when the investigator knows which items are compromised. This brief report proves that the SBNPL of Belov (2016) is equivalent to a statistic suggested for the same purpose by Drasgow, Levine, and Zickar 20 years ago.

Comparing the Performance of Eight Item Preknowledge Detection Statistics

Item preknowledge describes a situation in which a group of examinees (called aberrant examinees) have had access to some items (called compromised items) from an administered test prior to the exam. Item preknowledge negatively affects both the corresponding testing program and its users (e.g., universities, companies, government organizations) because scores for aberrant examinees are invalid. In general, item preknowledge is hard to detect due to multiple unknowns: unknown groups of aberrant examinees (at unknown test centers or schools) accessing unknown subsets of items prior to the exam. Recently, multiple statistical methods were developed to detect compromised items. However, the detected subset of items (called the suspicious subset) naturally has an uncertainty due to false positives and false negatives. The uncertainty increases when different groups of aberrant examinees had access to different subsets of items; thus, compromised items for one group are uncompromised for another group and vice versa. The impact of uncertainty on the performance of eight statistics (each relying on the suspicious subset) was studied. The measure of performance was based on the receiver operating characteristic curve. Computer simulations demonstrated how uncertainty combined with various independent variables (e.g., type of test, distribution of aberrant examinees) affected the performance of each statistic.