Making Life Choices Program: A psychoeducational intervention for promoting critical cognitive and affective competencies in adolescents at-risk for problem behaviors

The long term goal of the work described is to contribute to the emerging literature of prevention science in general, and to school-based psychoeducational interventions in particular. The psychoeducational intervention reported in this study used a main effects prevention intervention model. The current study focused on promoting optimal cognitive and affective functioning. The goal of this intervention was to increase potential protective factors such as critical cognitive and communicative competencies (e.g., critical problem solving and decision making) and affective competencies (e.g., personal control and responsibility) in middle adolescents who have been identified by the school system as being at-risk for problem behaviors. The current psychoeducational intervention draws on an ongoing program of theory and research (Berman, Berman, Cass Lorente, Ferrer Wreder, Arrufat, & Kurtines 1996; Ferrer Wreder, 1996; Kurtines, Berman, Ittel, & Williamson, 1995) and extends it to include Freire's (1970) concept of transformative pedagogy in developing school-based psychoeducational programs that target troubled adolescents. The results of the quantitative and qualitative analyses indicated trends that were generally encouraging with respect to the effects of the intervention on increasing critical cognitive and affective competencies. ^

Adolescent readers' self-perceptions of affective factors related to performance on standardized tests

The purpose of this study was to investigate which affective factors of adolescent high school readers were related to high-level readers, middle-level readers and low-level readers. The research problem was to determine the relationship between adolescent high school students' self-perceived reading self-efficacy factors and the students' reading performance on a standardized reading assessment considering demographic factors of age, gender and socio-economic status as covariates. The research design was ex post facto making inferences without direct intervention. The sample was obtained from one large, diverse, urban high school, consisting of 9th and 10th grade adolescent students (N = 176). Students voluntarily completed a self-report, reading self-efficacy survey. School records were used to obtain standardized reading level scores, age, gender, and socio-economic status data. An exploratory factor analysis of the self-efficacy survey responses resulted in the identification of 7 underlying factors. The striving (low-level) readers had significantly lower self-perceptions on 5 of the 7 affective factors than the middle-level readers, and strong (high-level) readers, p < .05. The 5 affective factors on which the striving readers had significantly lower self-perceptions were: (a) Observational Comparison, (b) Progress, (c) Lack of Progress, (d) Lack of Anxiety, and (e) Positive Social Feedback. The 2 affective factors which were not significantly different for reader level were Anxiety and Negative Social Feedback. Girls had significantly less anxiety than boys for both of the factors in the Anxiety category. Statistical results showed that none of the demographic covariates tested; age, gender, or socio-economic status, moderated the relationship between affective reader self-efficacy factors and reader level. This study concluded that there were distinguishable differences for striving, middle, and strong readers' self-efficacy factors. Determining affective factors related to reading can be used to create better instructional environments and instruction for adolescent students.

A digital signal processing approach for affective sensing of a computer user through pupil diameter monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

The impact of pre -training interventions on cognitive, skill, and affective learning outcomes: A meta -analytic examination of four decades of research

Technological advancements and the ever-evolving demands of a global marketplace may have changed the way in which training is designed, implemented, and even managed, but the ultimate goal of organizational training programs remains the same: to facilitate learning of a knowledge, skill, or other outcome that will yield improvement in employee performance on the job and within the organization (Colquitt, LePine, & Noe, 2000; Tannenbaum & Yukl, 1992). Studies of organizational training have suggested medium to large effect sizes for the impact of training on employee learning (e.g., Arthur, Bennett, Edens, & Bell, 2003; Burke & Day, 1986). However, learning may be differentially affected by such factors as the (1) level and type of preparation provided prior to training, (2) targeted learning outcome, (3) training methods employed, and (4) content and goals of training (e.g., Baldwin & Ford, 1988). A variety of pre-training interventions have been identified as having the potential to enhance learning from training and practice (Cannon-Bowers, Rhodenizer, Salas, & Bowers, 1998). Numerous individual studies have been conducted examining the impact of one or more of these pre-training interventions on learning. ^ I conducted a meta-analytic examination of the effect of these pre-training interventions on cognitive, skill, and affective learning. Results compiled from 359 independent studies (total N = 37,038) reveal consistent positive effects for the role of pre-training interventions in enhancing learning. In most cases, the provision of a pre-training intervention explained approximately 5–10% of the variance in learning, and in some cases, explained up to 40–50% of variance in learning. Overall attentional advice and meta-cognitive strategies (as compared with advance organizers, goal orientation, and preparatory information) seem to result in the most consistent learning gains. Discussion focuses on the most beneficial match between an intervention and the learning outcome of interest, the most effective format of these interventions, and the most appropriate circumstances under which these interventions should be utilized. Also highlighted are the implications of these results for practice, as well as propositions for important avenues for future research. ^

Workplace Discrimination Climate and Team Effectiveness: The Mediating Role of Collective Value Congruence, Team Cohesion, and Collective Affective Commitment

This study explored the relationship between workplace discrimination climate on team effectiveness through three serial mediators: collective value congruence, team cohesion, and collective affective commitment. As more individuals of marginalized groups diversify the workforce and as more organizations move toward team-based work (Cannon-Bowers & Bowers, 2010), it is imperative to understand how employees perceive their organization’s discriminatory climate as well as its effect on teams. An archival dataset consisting of 6,824 respondents was used, resulting in 332 work teams with five or more members in each. The data were collected as part of an employee climate survey administered in 2011 throughout the United States’ Department of Defense. The results revealed that the indirect effect through M1 (collective value congruence) and M2 (team cohesion) best accounted for the relationship between workplace discrimination climate (X) and team effectiveness (Y). Meaning, on average, teams that reported a greater climate for workplace discrimination also reported less collective value congruence with their organization (a1 = -1.07, p < .001). With less shared perceptions of value congruence, there is less team cohesion (d21 = .45, p < .001), and with less team cohesion there is less team effectiveness (b2 = .57, p < .001). In addition, because of theoretical overlap, this study makes the case for studying workplace discrimination under the broader construct of workplace aggression within the I/O psychology literature. Exploratory and confirmatory factor analysis found that workplace discrimination based on five types of marginalized groups: race/ethnicity, gender, religion, age, and disability was best explained by a three-factor model, including: career obstruction based on age and disability bias (CO), verbal aggression based on multiple types of bias (VA), and differential treatment based on racial/ethnic bias (DT). There was initial support to claim that workplace discrimination items covary not only based on type, but also based on form (i.e., nonviolent aggressive behaviors). Therefore, the form of workplace discrimination is just as important as the type when studying climate perceptions and team-level effects. Theoretical and organizational implications are also discussed.

A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.