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E-learning is increasingly used to support student learning in higher education. This results in huge amounts of item response data containing valuable information about students’ strengths and weaknesses that can be used to provide effective feedback to both students and teachers. However, in current practice, feedback in e-learning is often given in the form of a simple proportion of correctly solved items rather than diagnostic, actionable feedback. Diagnostic classification models (DCMs) provide opportunities to model the item response data from formative assessments in online learning environments and to obtain diagnostic information to improve teaching and learning. This simulation study explores the demands on the data structure (i.e., assessment length, respondent sample size) to apply log-linear DCMs to empirical data. Thereby we provide guidance to educational practitioners on how many items need to be administered to how many students in order to accurately assess skills at different levels of specificity using DCMs. In addition, effects of misspecification of the dimensionality of the assessed skills on model fit indices are explored. Results show that detecting these misspecifications statistically with DCMs can be problematic. Recommendations and implications for educational practice are discussed.

In the present study it is investigated whether students enrolled in different academic fields of study have differing personality traits (i.e., conscientiousness and openness) and whether the relationship between these traits and academic achievement differs by academic field. Using Structural Equation Modeling on data from a large sample of university students, this study examined to what extent students' levels of conscientiousness and openness differ by academic field and whether these personality traits have differential predictive value for academic achievement for students in different academic fields. We found that students who are more open to experience and less conscientious are more likely to enroll in a program in the academic field of arts/humanities than in another field. There were no differences in the predictive value of these personality traits for academic achievement by academic field when controlling for prior performance in high school. These findings emphasize the general effectiveness of conscientiousness in explaining academic achievement and also call for the consideration of academic fields or college majors in personality research. Besides having theoretical implications, these findings have practical implications for higher education.

E-learning is increasingly used to support student learning in higher education, facilitating administration of online formative assessments. Although providing diagnostic, actionable feedback is generally more effective, in current practice, feedback is often given in the form of a simple proportion of correctly solved items. This study shows the validation process of constructing detailed diagnostic information on a set of skills, abilities, and cognitive processes (so-called attributes) from students’ item response data with diagnostic classification models. Attribute measurement in the domain of statistics education is validated based on both expert judgment and empirical student data from a think-aloud study and large-scale assessment administration. The constructed assessments provide a valid and reliable measurement of the attributes. Inferences that can be drawn from the results of these formative assessments are discussed and it is demonstrated how this information can be communicated to students via learning dashboards to allow them to make more effective learning choices.

Self-licensing, employing reasons to justify indulgence, may help resolve the conflict between immediate temptations and long-term goals in favor of the former. It was hypothesized that this conflict-resolving potential of self-licensing may benefit self-regulation over time. With a momentary assessment design, we examined how self-licensing affects self-regulatory ability and the capacity to deal with subsequent self-regulatory conflicts. One hundred thirty-six female participants filled out surveys eight times per day for one week. Food temptation strength, conflict, resistance, and enactment were assessed, as well as license opportunity and perceived self-regulatory ability. When self-licensing opportunity was high (vs. low), a weaker association between temptation strength and conflict was observed. High license opportunity was associated with higher perceived self-regulatory ability for instances of low degrees of temptation enactment and predicted better handling of subsequent conflict after high degrees of prior temptation enactment. These results suggest that self-licensing can support self-regulation after initial failure.

Purpose This study examined who benefits most from a cognitive behavioural therapy (CBT)-based intervention that aims to enhance return to work (RTW) among employees who are absent due to common mental disorders (CMDs) (e.g., depression, anxiety, or adjustment disorder). We researched the influence of baseline work-related self-efficacy and mental health (depressive complaints and anxiety) on treatment outcomes of two psychotherapeutic interventions. Methods Using a quasi-experimental design, 12-month follow-up data of 168 employees were collected. Participants either received work-focused cognitive behavioural therapy (W-CBT) that integrated work aspects early into the treatment (n = 89) or regular cognitive behavioural therapy (R-CBT) without a focus on work (n = 79). Results Compared with R-CBT, W-CBT resulted in a faster partial RTW, irrespective of baseline self-efficacy. Among individuals with high self-efficacy, W-CBT also resulted in faster full RTW. The effectiveness of W-CBT on RTW did not depend on baseline depressive complaints or anxiety. The decline of mental health complaints did not differ between the two interventions, nor depended on baseline self-efficacy or mental health. Conclusions Considering the benefits of W-CBT for partial RTW, we recommend this intervention as a preferred method for employees with CMDs, irrespective of baseline self-efficacy, depression and anxiety. For individuals with high baseline self-efficacy, this intervention also results in higher full RTW. For those with low self-efficacy, extra exercises or components may be needed to promote full RTW. ispartof: JOURNAL OF OCCUPATIONAL REHABILITATION vol:29 issue:1 pages:31-41 ispartof: location:Netherlands status: published

Eye movement desensitization and reprocessing (EMDR) is an efficacious treatment for post-traumatic stress disorder. In EMDR, patients recall a distressing memory and simultaneously make eye movements (EM). Both tasks are considered to require limited working memory (WM) resources. Because this leaves fewer resources available for memory retrieval, the memory should become less vivid and less emotional during future recall. In EMDR analogue studies, a standardized procedure has been used, in which participants receive the same dual task manipulation of 1 EM cycle per second (1 Hz). From a WM perspective, the WM taxation of the dual task might be titrated to the WM taxation of the memory image. We hypothesized that highly vivid images are more affected by high WM taxation and less vivid images are more affected by low WM taxation. In study 1, 34 participants performed a reaction time task, and rated image vividness, and difficulty of retrieving an image, during five speeds of EM and no EM. Both a high WM taxing frequency (fast EM; 1.2 Hz) and a low WM taxing frequency (slow EM; 0.8 Hz) were selected. In study 2, 72 participants recalled three highly vivid aversive autobiographical memory images (n = 36) or three less vivid images (n = 36) under each of three conditions: recall + fast EM, recall + slow EM, or recall only. Multi-level modeling revealed a consistent pattern for all outcome measures: recall + fast EM led to less emotional, less vivid and more difficult to retrieve images than recall + slow EM and recall only, and the effects of recall + slow EM felt consistently in between the effects of recall + fast EM and recall only, but only differed significantly from recall + fast EM. Crucially, image vividness did not interact with condition on the decrease of emotionality over time, which was inconsistent with the prediction. Implications for understanding the mechanisms of action in memory modification and directions for future research are discussed.

Children of incarcerated mothers are considered at risk for disruptive behavior problems and later delinquency. Parenting may play a key role in this intergenerational transmission of delinquency. The present study aimed to evaluate the effectiveness of the Incredible Years parent training, enhanced with home visits, for (formerly) incarcerated mothers to prevent disruptive behavior problems in their 2- to 10-year-old children, by means of a nationwide randomized controlled trial. Mothers of 133 children (M age = 76.91 months; 48.9% boys) were assigned to an intervention, consisting of group sessions and individual home visits, or a no-intervention control group. The intervention yielded significant effects on parenting and child behavior for maternal report. Marginally significant effects on child behavior were found for teacher report. The results show short-term effectiveness of parent training for the high-risk and hard-to-reach population of (formerly) incarcerated mothers and their children.

Sometimes it can be challenging to collect enough data. Think of naturally small populations, such as people with rare diseases. Or hard to access target groups, such as people with addiction problems or undocumented migrants. However, all statistical methods require a certain amount of data to perform well. Recently, more and more often researchers with small samples switch to a Bayesian approach to deal with analysis problems. In the Bayesian framework, observed data is combined with prior knowledge (e.g., knowledge based on opinions of experts in the field or based on previous studies). This prior knowledge is captured in a distribution, the so-called prior distribution. The studies in this dissertation show that the switch to a Bayesian approach is not without problems. Some software programs offer built-in default prior distributions, which are unfortunately not always suitable and can lead to incorrect results when samples are small. The use of Bayesian statistics in that case is not a solution to analyse a small sample. Blindly relying on built-in default priors is not an option when samples are small. The smaller the sample size, the more important the prior distributions. In Chapter 1 of this thesis, the results are presented from an extensive systematic literature review on the performance of Bayesian and frequentist estimation methods under small samples for Structural Equation Models (SEMs). Based on this literature review, we conclude that with small samples the use of Bayesian methods with only default priors can lead to severely biased results. We end the chapter with recommendations for researchers on analysing a small sample size and on how to specify thoughtful prior distributions. In Chapters 2 and 3, we discuss the results of simulation studies. Based on these studies we advise researchers with small samples to specify informative priors. We recommend researchers take the most careful approach possible: start with carefully constructing prior distributions; and assess the impact and robustness of the specified priors through an extensive sensitivity analysis. When researchers are not able or willing to include prior information, we advise to use the twostep method or factor regression score. These methods are a safer choice than maximum likelihood estimation, as those led to higher convergence rates without negative variances, more stable results across replications and less extreme parameter estimates than maximum likelihood estimation with small samples. In the final chapter, we discuss in a non-technical tutorial the risks of using Bayesian estimation while blindly relying on built-in software default priors when samples are small. Also, we demonstrate an online educational Shiny app, in which users can play around with varying sample sizes and prior settings to investigate the impact of priors on the results. In addition, we provide guidelines on how to use Bayesian SEM in a thoughtful way when samples are small.

In the Netherlands, every year a substantial proportion of first-year students in higher education drop out due to a wrong program choice. Matching has been introduced to decrease wrong choices. In this dissertation we studied various types of matching procedures using questionnaire and study progress data from four Dutch universities as well as interviews with (prospective) students. Based on a perspective of person-environment fit, we used three aspects that are important in determining whether students fit with the program of their choice: 1) interests, 2) believe in their abilities, and 3) feeling at home in the program. In this dissertation, we examined the effectiveness of different types of matching procedures as experienced by students, and in relation to final enrollment and first-year student success. The interviews revealed that students believe that matching procedures can contribute to their understanding of the program. In general, the more aspects of fit with the program students could test in matching activities, the more useful the activity was found to be. Moreover, quantitative analyses showed that the types of matching that were found to be more useful by students involved programs where fewer students followed through on their initial enrollment; an indication that more students changed their minds after participating in a matching procedure. Enrollment rates have decreased since the introduction of matching for programs with intensive procedures, but not for less intensive programs, i.e. programs that only offer follow-up activities of questionnaire completion for students identified as at-risk based. Therefore, the introduction of matching procedures in general, and specifically the intensity of these procedures is a likely explanation for lower enrollment rates. Furthermore, we analyzed information from the matching questionnaires for the prediction of study success. It was found that indicators of fit, measured prior to the start of the program, were predictive of grade point average and number of credits earned in the first year of study. Moreover, most fit indicators in our study differed in strength and/or direction between STEM and non-STEM programs. For both STEM and non-STEM students, high school grade point average is the strongest predictor of academic success in the first year of study. However, our findings provide evidence that non-cognitive indicators, such as conscientiousness and interest in the program, are stronger predictors of academic success in the first year for non-STEM students than for STEM students. Lastly, we found that text-mining analyses of motivational texts in the matching questionnaires predicted first-year dropout as well as a set of student characteristics. However, when the motivational texts and student characteristics were combined, the prediction of dropout did not improve. Thus, on the one hand, the use of text mining techniques for dropout prediction seems promising. On the other hand, the fact that combining the text and numerical data does not improve the prediction of dropout in this study could indicate that they measure the same underlying concepts. Combining the findings of all studies provides interesting leads for improving matching procedures, for example in terms of necessary elements in these procedures.

In the Netherlands, every year a substantial proportion of first-year students in higher education drop out due to a wrong program choice. Matching has been introduced to decrease wrong choices. In this dissertation we studied various types of matching procedures using questionnaire and study progress data from four Dutch universities as well as interviews with (prospective) students. Based on a perspective of person-environment fit, we used three aspects that are important in determining whether students fit with the program of their choice: 1) interests, 2) believe in their abilities, and 3) feeling at home in the program. In this dissertation, we examined the effectiveness of different types of matching procedures as experienced by students, and in relation to final enrollment and first-year student success. The interviews revealed that students believe that matching procedures can contribute to their understanding of the program. In general, the more aspects of fit with the program students could test in matching activities, the more useful the activity was found to be. Moreover, quantitative analyses showed that the types of matching that were found to be more useful by students involved programs where fewer students followed through on their initial enrollment; an indication that more students changed their minds after participating in a matching procedure. Enrollment rates have decreased since the introduction of matching for programs with intensive procedures, but not for less intensive programs, i.e. programs that only offer follow-up activities of questionnaire completion for students identified as at-risk based. Therefore, the introduction of matching procedures in general, and specifically the intensity of these procedures is a likely explanation for lower enrollment rates. Furthermore, we analyzed information from the matching questionnaires for the prediction of study success. It was found that indicators of fit, measured prior to the start of the program, were predictive of grade point average and number of credits earned in the first year of study. Moreover, most fit indicators in our study differed in strength and/or direction between STEM and non-STEM programs. For both STEM and non-STEM students, high school grade point average is the strongest predictor of academic success in the first year of study. However, our findings provide evidence that non-cognitive indicators, such as conscientiousness and interest in the program, are stronger predictors of academic success in the first year for non-STEM students than for STEM students. Lastly, we found that text-mining analyses of motivational texts in the matching questionnaires predicted first-year dropout as well as a set of student characteristics. However, when the motivational texts and student characteristics were combined, the prediction of dropout did not improve. Thus, on the one hand, the use of text mining techniques for dropout prediction seems promising. On the other hand, the fact that combining the text and numerical data does not improve the prediction of dropout in this study could indicate that they measure the same underlying concepts. Combining the findings of all studies provides interesting leads for improving matching procedures, for example in terms of necessary elements in these procedures.