Learning About Parenting Together: A Programme to Support Parents with Inter-generational Concerns in Pune,India

Rapid developments in the last few decades have brought about dramatic changes in Indian social life, particularly affecting new middle-class families. Inter-generational conflicts, high academic pressures, and modern anxieties lead to stress both in parents and in children. There is a need for parenting programmes that respond to these specific concerns, in order to reduce parenting stress and improve family well-being. This study aimed to develop and evaluate a parenting programme in Pune, India, based on a ‘theory of change’. In this pilot programme, parents were encouraged to learn in a group format about fostering autonomy in children, promoting academic potential in a stress-free manner and remaining in connection with adolescents. Facilitated by a psychologist, parents participated in four sessions involving ‘creativity and play’, ‘self-awareness and bonding’, and ‘communication’. The intervention was based on validated psychotherapeutic approaches and parenting methods to support parents in their learning. Some 16 in-depth interviews were conducted with parents before and eight interviews after the intervention to understand their learning experiences. Additional feedback was gathered from observation notes and debriefings after each session. The results show that the attention for playful quality time helped parents to (re)gain a more creative and flexible attitude towards spending time with their children. Second, parents learned to reflect on their frameworks of meaning (rooted in their own upbringing), listen attentively and communicate more mindful with their children. Furthermore, this study shows that an activity-based approach, connecting parents in co-creating new parenting paradigms, while supporting them with stress-reducing tools, is a useful way of engaging this target group. The study concludes by offering new perspectives for counsellors working with families in countries undergoing rapid change.     

Disputing Irrational Beliefs Among Convicted Terrorists and Extremist Beliefs

This study investigated the most common irrational beliefs among samples of: (1) convicted terrorists and (2) extremists, all of whom were prisoners in the country of Jordan. It also investigated the effectiveness of a program of disputing irrational beliefs (DIBP) with the same populations. The subjects were assigned randomly to two groups: an experimental group (EG) that consisted of 43 prisoners who received an training program, and a control group that consisted of 48 prisoners who received no training. The irrational beliefs scale (IBS) scores were assessed for both groups before and after exposure to the DIBP. To test the hypotheses of the study, means and standard deviations of the IBS measures were submitted to a two-way analysis of covariance. The results revealed that the most common irrational belief loaded on the factors of musts, exaggerations, and perfectionism. Moreover, the results revealed significant differences between the means of the two groups on the total IBS scores at post-test, with the EG scoring lower, which indicated the effectiveness of the training program in reducing the level of IBS. The results yielded significant differences in the effect of the reason for imprisonment in favor of extremists and the interaction between the reason for prison and treatment in favor of the EG and extremists.     

The Relationship Between Automatic Thoughts and Irrational Beliefs Predicting Anxiety and Depression

Cognitive behavioral approaches differ in their views on core cognitions and their hypothesized role in the etiology of depression and anxiety. The present study provides empirical evidence regarding the relationship between irrational beliefs and components of automatic thoughts and their role in the etiology of depression and anxiety. The present study utilized newer and improved questionnaires to assess components of irrational belief. Based on prior research by Safren et al. (Cogn Ther Res 24(3):327–344, 2000), a three-factor structure of the combined automatic thought questionnaires were utilized to measure components of automatic thoughts as they relate to depression and anxiety. Factor analytical methods were utilized to confirm the factor structure of the irrational beliefs and automatic thoughts components. Advanced path modeling was utilized to model the relationship between irrational beliefs and automatic thoughts in predicting anxiety and depression. The study used a sample of N = 542 undergraduate psychology students during stressful exam times. Results indicated that the irrational belief Demandingness represents a primary factor, followed by the secondary irrational beliefs as proposed by Rational Emotive Behavioral Theory. Selfdowning beliefs were fully mediated by depressive automatic thoughts in the case of depressive affect. Low frustration tolerance contributed unique variance to anxious and depressive affect that was not fully mediated by automatic thoughts. Results from the present study add empirical evidence that irrational beliefs indeed represent core and intermediary beliefs that lead to specific automatic thoughts, which is congruent with cognitive behavioral theory as proposed by Rational Emotive Behavioral Therapy.     

The future is now: the impact of present fluency in judgments about the future

Recent research has emphasised the role of episodic memory in both remembering past events and in envisaging future events. On the other hand, it has been repeatedly shown that judgments about past events are affected by the fluency with which retrieval cues are processed. In this paper we investigate whether perceptual fluency also plays a role in judgments about future events. For this purpose we conducted four experiments. The first experiment replicated recent findings showing that stimuli that are processed fluently tend to be wrongly recognised as having been encountered in the past outside the laboratory walls [Brown, A. S., & Marsh, E. J. (2009). Creating illusions of past encounter through brief exposure. Psychological Science, 20, 534–538. doi:10.1111/j.1467-9280.2009.02337.x]. Two follow-up experiments using Brown and Marsh’s [(2009). Creating illusions of past encounter through brief exposure. Psychological Science, 20, 534–538. doi:10.1111/j.1467-9280.2009.02337] task tested the influence of perceptual fluency on future judgments. The fourth and last experiment was designed to rule out a potential confounding factor in the two previous experiments. Across experiments, we found that people rely on fluency when making judgments about events that are yet to come. These results suggest that fluency is an equally valid cue for past and future judgments.     

The Madonna-Whore Dichotomy: Men Who Perceive Women's Nurturance and Sexuality as Mutually Exclusive Endorse Patriarchy and Show Lower Relationship Satisfaction

The Madonna-Whore Dichotomy (MWD) denotes polarized perceptions of women in general as either “good,” chaste, and pure Madonnas or as “bad,” promiscuous, and seductive whores. Whereas prior theories focused on unresolved sexual complexes or evolved psychological tendencies, feminist theory suggests the MWD stems from a desire to reinforce patriarchy. Surveying 108 heterosexual Israeli men revealed a positive association between MWD endorsement and patriarchy-enhancing ideology as assessed by Social Dominance Orientation (preference for hierarchical social structures), Gender-Specific System Justification (desire to maintain the existing gender system), and sexist attitudes (Benevolent and Hostile Sexism, Sexual Objectification of Women, and Sexual Double Standards). In addition, MWD endorsement negatively predicted men’s romantic relationship satisfaction. These findings support the feminist notion that patriarchal arrangements have negative implications for the well-being of men as well as women. Specifically, the MWD not only links to attitudes that restrict women’s autonomy, but also impairs men’s most intimate relationships with women. Increased awareness of motives underlying the MWD and its psychological costs can help practice professionals (e.g., couple therapists), as well as the general public, to foster more satisfying heterosexual relationships.     

Gender Bias Produces Gender Gaps in STEM Engagement

We explored whether the existence of gender bias causes gender gaps in STEM engagement. In Experiment 1 (n?=?322), U.S. women projected less sense of belonging, positivity toward, and aspirations to participate in STEM than did men when exposed to the reality of STEM gender bias. These gender differences disappeared when participants were told that STEM exhibits gender equality, suggesting that gender bias produces STEM gender gaps. Experiment 2 (n?=?429) explored whether results generalized to a specific STEM department, and whether organizational efforts to mitigate gender bias might shrink gender gaps. U.S. women exposed to a biased chemistry department anticipated more discrimination and projected less sense of belonging, positive attitudes and trust and comfort than did men. These gender differences vanished when participants read about an unbiased department, again suggesting that gender bias promotes STEM gender gaps. Further, moderated mediation analyses suggested that in the presence of gender bias (but not gender equality), women projected less positive attitudes and trust and comfort than did men because they experienced less sense of belonging and anticipated more discrimination. Results were largely unaffected by whether departments completed a diversity training, suggesting that knowledge of diversity initiatives alone cannot close STEM gender gaps.     

Developing a Learning Progression for Curriculum,Instruction, and Student Learning: An Example from Mathematics Education

Learning progressions have been demarcated by some for science education, or only concerned with levels of sophistication in student thinking as determined by logical analyses of the discipline. We take the stance that learning progressions can be leveraged in mathematics education as a form of curriculum research that advances a linked understanding of students learning over time through careful articulation of a curricular framework and progression, instructional sequence, assessments, and levels of sophistication in student learning. Under this broadened conceptualization, we advance a methodology for developing and validating learning progressions, and advance several design considerations that can guide research concerned with engendering forms of mathematics learning, and curricular and instructional support for that learning. We advance a two-phase methodology of (a) research and development, and (b) testing and revision. Each phase involves iterative cycles of design and experimentation with the aim of developing a validated learning progression. In particular, we gathered empirical data to revise our hypothesized curricular framework and progression and to measure change in students. thinking over time as a means to validate both the effectiveness of our instructional sequence and of the assessments designed to capture learning. We use the context of early algebra to exemplify our approach to learning progressions in mathematics education with a focus on the concept of mathematical equivalence across Grades 3-5. The domain of work on research on learning over time is evolving; our work contributes a broadened role for learning progressions work in mathematics education research and practice.     

Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation

Measurement invariance is a fundamental assumption in item response theory models, where the relationship between a latent construct (ability) and observed item responses is of interest. Violation of this assumption would render the scale misinterpreted or cause systematic bias against certain groups of persons. While a number of methods have been proposed to detect measurement invariance violations, they typically require advance definition of problematic item parameters and respondent grouping information. However, these pieces of information are typically unknown in practice. As an alternative, this paper focuses on a family of recently proposed tests based on stochastic processes of casewise derivatives of the likelihood function (i.e., scores). These score-based tests only require estimation of the null model (when measurement invariance is assumed to hold), and they have been previously applied in factor-analytic, continuous data contexts as well as in models of the Rasch family. In this paper, we aim to extend these tests to two-parameter item response models, with strong emphasis on pairwise maximum likelihood. The tests’ theoretical background and implementation are detailed, and the tests’ abilities to identify problematic item parameters are studied via simulation. An empirical example illustrating the tests’ use in practice is also provided.     

Automated Item Generation with Recurrent Neural Networks

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven’s progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.