Patterns and effects of notetaking by learning -disabled and nondisabled adolescents: A comparative study

This study analyzes the qualitative and quantitative patterns of notetaking by learning disabled (LD) and nondisabled (ND) adolescents and the effectiveness of notetaking and review as measured by the subjects' ability to recall information presented during a lecture. The study also examines relationships between certain learner characteristics and notetaking. The following notetaking variables were investigated: note completeness, number of critical ideas recorded, levels of processing information, organizational strategies, fluency of notes, and legibility of notes. The learner characteristics examined pertained to measures on achievement, short-term memory, listening comprehension, and verbal ability.^ Students from the 11th and 12th grades were randomly selected from four senior high schools in Dade County, Florida. Seventy learning disabled and 79 nondisabled subjects were shown a video tape lecture and required to take notes. The lecture conditions controlled for presentation rate, prior knowledge, information density, and difficulty level. After 8 weeks, their notes were returned to the subjects for a review period, and a posttest was administered.^ Results of this study suggest significant differences (p $\le$.01) in the patterns of notetaking between LD and ND groups not due to differences in the learner characteristics listed above. In addition, certain notetaking variables such as process levels, number of critical ideas, and note completeness were found to be significantly correlated to learning outcome. Further, deficiencies in the spontaneous use of organizational strategies and abbreviations adversely affected the notetaking effectiveness of learning disabled students.^ Both LD and ND subjects recalled more information recorded in their notes than not recorded. This difference was significant only for the ND group. By contrast, LD subjects compensated for their poor notetaking skills and recalled significantly more information not recorded on their notes than did ND subjects. The major implications of these findings suggest that LD and ND subjects exhibit very different entry behaviors when asked to perform a notetaking task; hence, teaching approaches to notetaking must differ as well. ^

Differential effects of single -sex versus coed education on the mathematical reasoning ability, verbal reasoning ability, and self-concept of high school girls

This study explored the differential effects of single-sex versus coed education on the cognitive and affective development of young women in senior year of high school. The basic research question was: What are the differential effects of single-sex versus coed education on the development of mathematical reasoning ability, verbal reasoning ability, or self-concept of high school girls?^ This study was composed of two parts. In the first part, the SAT verbal and mathematical ability scores were recorded for those subjects in the two schools from which the sample populations were drawn. The second part of the study required the application of the Piers-Harris Children's Self-Concept Scale to subjects in each of the two sample populations. The sample schools were deliberately selected to minimize between group differences in the populations. One was an all girls school, the other coeducational.^ The research design employed in this study was the causal-comparative method, used to explore causal relationships between variables that already exist. Based on a comprehensive analysis of the data produced by this research, no significant difference was found to exist between the mean scores of the senior girls in the single-sex school and the coed school on the SAT 1 verbal reasoning section. Nor was any significant difference found to exist between the mean scores of the senior girls in the single-sex school and the coed school on the SAT 1 mathematical reasoning section. Finally, no significant difference between the mean total scores of the senior girls in the single-sex school and the coed school on the Piers-Harris Children's Self-Concept Scale was found to exist.^ Contrary to what many other studies have found in the past about single-sex schools and their advantages for girls, this study found no support for such advantages in the cognitive areas of verbal and mathematical reasoning as measured by the SAT or in the affective area of self-concept as measured by the Piers-Harris Children's Self-Concept Scale. ^

Detecting deception: Identifying differences in liars' and truth tellers' verbal strategies

Deception research has traditionally focused on three methods of identifying liars and truth tellers: observing non-verbal or behavioral cues, analyzing verbal cues, and monitoring changes in physiological arousal during polygraph tests. Research shows that observers are often incapable of discriminating between liars and truth tellers with better than chance accuracy when they use these methods. One possible explanation for observers' poor performance is that they are not properly applying existing lie detection methods. An alternative explanation is that the cues on which these methods — and observers' judgments — are based do not reliably discriminate between liars and truth tellers. It may be possible to identify more reliable cues, and potentially improve observers' ability to discriminate, by developing a better understanding of how liars and truth tellers try to tell a convincing story. ^ This research examined (a) the verbal strategies used by truthful and deceptive individuals during interviews concerning an assigned activity, and (b) observers' ability to discriminate between them based on their verbal strategies. In Experiment I, pre-interview instructions manipulated participants' expectations regarding verifiability; each participant was led to believe that the interviewer could check some types of details, but not others, before deciding whether the participant was being truthful or deceptive. Interviews were then transcribed and scored for quantity and type of information provided. In Experiment II, observers listened to a random sample of the Experiment I interviews and rendered veracity judgments; half of the observers were instructed to judge the interviews according to the verbal strategies used by liars and truth tellers and the other half were uninstructed. ^ Results of Experiment I indicate that liars and truth tellers use different verbal strategies, characterized by a differential amount of detail. Overall, truthful participants provided more information than deceptive participants. This effect was moderated by participants' expectations regarding verifiability such that truthful participants provided more information only with regard to verifiable details. Results of Experiment II indicate that observers instructed about liars' and truth tellers' verbal strategies identify them with greater accuracy than uninstructed observers. ^

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^