The effect of attitudes towards individuals with sexual convictions on professional and student risk judgments

Attitudes towards individuals with sexual convictions is an area with growing research interest, but the effects of such attitudes on professional judgments is largely unexplored. What is known from the existing literature is that attitudes guide the interpretation of sexual crime related information, which cascade into potential biased or heuristically driven judgments. In this study we recruited samples of both students (n = 341) and forensic professionals (n = 186) to explore whether attitudes towards individuals with sexual convictions predicted risk judgments of hypothetical sexual offense scenarios, and whether this relationship is moderated by professional status or perpetrator characteristics. Forensic professionals expressed more positive attitudes overall, but the significant effect of attitudes on risk judgments was consistent between participant groups and was not moderated by perpetrator age or sex. We suggest that relying on attitudes as a basis for risk judgments opens the door to incorrect (and potentially dangerous) decision-making and discuss our data in terms of their potential clinical implications.

Differential Item and Test Functioning of the SRSS-IE12 Across Race, Ethnicity, Gender, and Elementary Level

This study used item response theory (IRT) methods to investigate group differences in responses to the 12-item Student Risk Screening Scale-Internalizing and Externalizing (SRSS-IE12) in a sample of 3,837 elementary school students. Using factor analysis and graded response models from IRT methods, we examined the factor structure, and item and test functioning of the SRSS-IE12. The SRSS-IE12 internalizing and externalizing factors reflected the hypothesized two-factor model. IRT analyses indicated that SRSS-IE12 items and tests measure internalizing and externalizing traits similarly across students from different race, ethnicity, gender, and elementary level (K-2 vs. 3-5) groups. Moreover, the mostly negligible differential item functioning (DIF) and differential test functioning (DTF) observed suggest these scales render equitable trait ratings. Collectively, the results provide further support for the SRSS-IE12 for universal screening in racially diverse elementary schools.

Predictive Validity of the Student Risk Screening Scale-Internalizing and Externalizing (SRSS-IE) Scores

Schools have become the most common setting for youth to receive additional supports—including access to mental health services serving as an entry point for identification and subsequent provision of these services. Many schools rely on office discipline referral (ODR) or suspension data as informal screeners for students who may need additional supports. Discipline data may be effective at identifying students with externalizing behaviors, yet students with internalizing behaviors may not engage in behaviors that warrant an ODR or suspension. As such, these students may go undetected in the absence of systematic screenings. This study explored whether a universal behavior screener identified students at risk for externalizing and internalizing concerns who may not have been identified through ODRs. The Student Risk Screening Scale for Internalizing/Externalizing (SRSS-IE) was completed for 1,201 elementary students in 3 elementary schools. SRSS-IE data were used to predict year-end ODRs. Results showed externalizing scores (SRSS-E7) on the SRSS-IE predicted year-end ODRs, with higher SRSS-E7 scores predicting with more ODRs. However, internalizing scores (SRSS-I5) on the SRSS-IE did not significantly predict year-end ODRs suggesting ODRs may not be an effective data source for identifying students with internalizing behaviors absent of externalizing behaviors.

Using Interval Likelihood Ratios in Gated Screening: A Direct Replication Study

This direct replication study compared the use of dichotomized likelihood ratios and interval likelihood ratios, derived using a prior sample of students, for predicting math risk in middle school. Data from the prior year state test and the Measures of Academic Progress were analyzed to evaluate differences in the efficiency and diagnostic accuracy of gated screening decisions. Post-test probabilities were interpreted using a threshold decision-making model to classify student risk during screening. Using interval likelihood ratios led to fewer students requiring additional testing after the first gate. But, when interval likelihood ratios were used, three tests were required to classify sixth and seventh grade students as at-risk or not at-risk. Only two tests were needed to classify students as at-risk or not at-risk when dichotomized likelihood ratios were used. Acceptable sensitivity and specificity estimates were obtained, regardless of the type of likelihood ratios used to estimate post-test probabilities. When predicting academic risk, interval likelihood ratios may be best reserved for situations where at least three successive tests are available to be used in a gated screening model.

The Student Risk Screening Scale for Early Childhood: Additional Validation Studies

We report findings of two validation studies of the Student Risk Screening Scale for Early Childhood (SRSS-EC). Although previous exploratory inquiry suggested a two-factor solution, results of Study 1 conducted with 274 preschool-age youth suggested mixed support for a two-factor model, with only one fit index suggesting an adequate fit (incremental index, comparative fit index [CFI] = 0.963). However, results did provide additional evidence of convergent validity between SRSS-EC and Strengths and Difficulties Questionnaire scores. In Study 2, results of convergent validity studies with 90 preschool-age children offered evidence between SRSS-EC and (a) Social Skills Improvement System—Performance Screening Guide and (b) Caregiver—Teacher Report Form (C-TRF) scores. Results suggested the following cutting scores: SRSS-EC E7 (0–5 low, 6–7 moderate, 8–21 high risk) and SRSS-EC I4 (0–2 low, 3–4 moderate, and 5–12 high risk), with C-TRF scores as the criterion. We discuss limitations and directions for future inquiry.

Student risk identification learning model using machine learning approach

<p class="Abstract">Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall.</p>