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Deliberative and paternalistic interaction styles for conversational agents in digital health
(2021)
Background:
Recent years have witnessed a constant increase in the number of people with chronic conditions requiring ongoing medical support in their everyday lives. However, global health systems are not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. Moreover, different aspects of CAs have not yet been sufficiently investigated to fully exploit their potential. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the interaction between patients and physicians. Patient-physician interaction is recognized as a critical success factor for patient satisfaction, treatment adherence, and subsequent treatment outcomes. However, so far, it remains effectively unknown how different interaction styles can be implemented into CA interactions and whether these styles are recognizable by users.
Objective:
The objective of this study was to develop an approach to reproducibly induce 2 specific interaction styles into CA-patient dialogs and subsequently test and validate them in a chronic health care context.
Methods:
On the basis of the Roter Interaction Analysis System and iterative evaluations by scientific experts and medical health care professionals, we identified 10 communication components that characterize the 2 developed interaction styles: deliberative and paternalistic interaction styles. These communication components were used to develop 2 CA variations, each representing one of the 2 interaction styles. We assessed them in a web-based between-subject experiment. The participants were asked to put themselves in the position of a patient with chronic obstructive pulmonary disease. These participants were randomly assigned to interact with one of the 2 CAs and subsequently asked to identify the respective interaction style. Chi-square test was used to assess the correct identification of the CA-patient interaction style.
Results:
A total of 88 individuals (42/88, 48% female; mean age 31.5 years, SD 10.1 years) fulfilled the inclusion criteria and participated in the web-based experiment. The participants in both the paternalistic and deliberative conditions correctly identified the underlying interaction styles of the CAs in more than 80% of the assessments (X-1(,8)8(2)=38.2; P<.001; phi coefficient r(phi)=0.68). The validation of the procedure was hence successful.
Conclusions:
We developed an approach that is tailored for a medical context to induce a paternalistic and deliberative interaction style into a written interaction between a patient and a CA. We successfully tested and validated the procedure in a web-based experiment involving 88 participants. Future research should implement and test this approach among actual patients with chronic diseases and compare the results in different medical conditions. This approach can further be used as a starting point to develop dynamic CAs that adapt their interaction styles to their users.
Detrimental effects of adverse family conditions for children's wellbeing are well-documented, but little is known about the impact of specific risk factors, or about potential protective factors that buffer the effects of family risk factors on negative development.
We investigated the impact of five important family risk factors (e.g., parental conflict) on internalizing and externalizing problems and the potential buffering effects of peer acceptance and academic skills at two measurement points two years apart in 1195 7-to 10-year-olds (T1: M-Age = 8.54).
Latent change models showed that increases in risk factors over the two years predicted increasing internalizing and externalizing problems. Parental conflict was the most impactful risk factor, although peer acceptance and academic skills showed some buffering effects.
The results highlight the necessity of investigating cumulative and single risk factors, specifically interparental conflict, and emphasize the need to strengthen children's internal and social resources to buffer the effects of adverse family conditions.
In the present paper we empirically investigate the psychometric properties of some of the most famous statistical and logical cognitive illusions from the "heuristics and biases" research program by Daniel Kahneman and Amos Tversky, who nearly 50 years ago introduced fascinating brain teasers such as the famous Linda problem, the Wason card selection task, and so-called Bayesian reasoning problems (e.g., the mammography task). In the meantime, a great number of articles has been published that empirically examine single cognitive illusions, theoretically explaining people's faulty thinking, or proposing and experimentally implementing measures to foster insight and to make these problems accessible to the human mind. Yet these problems have thus far usually been empirically analyzed on an individual-item level only (e.g., by experimentally comparing participants' performance on various versions of one of these problems). In this paper, by contrast, we examine these illusions as a group and look at the ability to solve them as a psychological construct. Based on an sample of N = 2,643 Luxembourgian school students of age 16-18 we investigate the internal psychometric structure of these illusions (i.e., Are they substantially correlated? Do they form a reflexive or a formative construct?), their connection to related constructs (e.g., Are they distinguishable from intelligence or mathematical competence in a confirmatory factor analysis?), and the question of which of a person's abilities can predict the correct solution of these brain teasers (by means of a regression analysis).
It is well-documented that academic achievement is associated with students' self-perceptions of their academic abilities, that is, their academic self-concepts. However, low-achieving students may apply self-protective strategies to maintain a favorable academic self-concept when evaluating their academic abilities. Consequently, the relation between achievement and academic self-concept might not be linear across the entire achievement continuum. Capitalizing on representative data from three large-scale assessments (i.e., TIMSS, PIRLS, PISA; N = 470,804), we conducted an integrative data analysis to address nonlinear trends in the relations between achievement and the corresponding self-concepts in mathematics and the verbal domain across 13 countries and 2 age groups (i.e., elementary and secondary school students). Polynomial and interrupted regression analyses showed nonlinear relations in secondary school students, demonstrating that the relations between achievement and the corresponding self-concepts were weaker for lower achieving students than for higher achieving students. Nonlinear effects were also present in younger students, but the pattern of results was rather heterogeneous. We discuss implications for theory as well as for the assessment and interpretation of self-concept.
Due to the COVID-19 pandemic, all schools in Germany were locked down for several months in 2020. How schools realized teaching during the school lockdown greatly varied from school to school. N = 2,647 parents participated in an online survey and rated the following activities of teachers in mathematics, language arts (German), English, and science / biology during the school lockdown: frequency of sending task assignments, task solutions and requesting for solutions, giving task-related feedback, grading tasks, providing lessons per videoconference, and communicating via telecommunication tools with students and / or parents. Parents also reported student academic outcomes during the school lockdown (child's learning motivation, competent and independent learning, learning progress). Parents further reported student characteristics and social background variables: child's negative emotionality, school engagement, mathematical and language competencies, and child's social and cultural capital. Data were separately analyzed for elementary and secondary schools. In both samples, frequency of student-teacher communication was associated with all academic outcomes, except for learning progress in elementary school. Frequency of parent-teacher communication was associated with motivation and learning progress, but not with competent and independent learning, in both samples. Other distant teaching activities were differentially related to students' academic outcomes in elementary vs. secondary school. School engagement explained most additional variance in all students' outcomes during the school lockdown. Parent's highest school leaving certificate incrementally predicted students' motivation, and competent and independent learning in secondary school, as well as learning progress in elementary school. The variable "child has own bedroom" additionally explained variance in students' competent and independent learning during the school lockdown in both samples. Thus, both teaching activities during the school lockdown as well as children's characteristics and social background were independently important for students' motivation, competent and independent learning, and learning progress. Results are discussed with regard to their practical implications for realizing distant teaching.
The evaluation of how (human) individuals perceive robots is a central issue to better understand human-robot interaction (HRI). On this topic, promising proposals have emerged. However, present tools are not able to assess a sufficient part of the composite psychological dimensions involved in the evaluation of HRI. Indeed, the percentage of variance explained is often under the recommended threshold for a construct to be valid. In this article, we consolidate the lessons learned from three different studies and propose a further developed questionnaire based on a multicomponent approach of anthropomorphism by adding traits from psychosocial theory about the perception of others and the attribution and deprivation of human characteristics: the de-humanization theory. Among these characteristics, the attribution of agency is of main interest in the field of social robotics as it has been argued that robots could be considered as intentional agents. Factor analyses reveal a four sub-dimensions scale including Sociability, Agency, Animacy, and the Disturbance. We discuss the implication(s) of these dimensions on future perception of and attitudes towards robots.
In this article, we address the measurement of individualized instruction in the context of regular classroom instruction. Our study assessed instructional practices geared towards individualization in German third grade reading lessons by combining self-report data from 621 students, from their teachers (n = 57), and live obser-vations. We then investigated the reliability of these different approaches to measuring individualization as well as the agreement between them. All three approaches yielded reliable indicators of individualized practices, but not all of them corresponded with each other. We found considerable agreement between students and observers, but neither agreed with teachers' self-reports. Upon closer examination, we found that students' ratings only correlated with teacher ratings that were provided close to the timepoint of interest. This correlation increased when teacher measures were corrected for response tendencies. We conclude with some recommendations for future studies that aim to measure individualized instruction in the classroom.
Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and competencies. For example, research in assessing science teachers' noticing and attention to classroom events benefitted from more open-ended response formats because teachers can present their own accounts. Then, open-ended responses are typically analyzed with some form of content analysis. However, language is noisy, ambiguous, and unsegmented and thus open-ended, constructed responses are complex to analyze. Uncovering patterns in these responses would benefit from more principled and systematic analysis tools. Consequently, computer-based methods with the help of machine learning and natural language processing were argued to be promising means to enhance assessment of noticing skills with constructed response formats. In particular, pretrained language models recently advanced the study of linguistic phenomena and thus could well advance assessment of complex constructs through constructed response items. This study examines potentials and challenges of a pretrained language model-based clustering approach to assess preservice physics teachers' attention to classroom events as elicited through open-ended written descriptions. It was examined to what extent the clustering approach could identify meaningful patterns in the constructed responses, and in what ways textual organization of the responses could be analyzed with the clusters. Preservice physics teachers (N = 75) were instructed to describe a standardized, video-recorded teaching situation in physics. The clustering approach was used to group related sentences. Results indicate that the pretrained language model-based clustering approach yields well-interpretable, specific, and robust clusters, which could be mapped to physics-specific and more general contents. Furthermore, the clusters facilitate advanced analysis of the textual organization of the constructed responses. Hence, we argue that machine learning and natural language processing provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods.
Background:
Using the internet to search for information or share images about self-harm is an emerging risk among young people. The aims of this study were (a) to analyze the prevalence of different types of self-harm on the internet and differences by sex and age, and (b) to examine the relationship of self-harm on the internet with intrapersonal factors (i.e., depression and anxiety) and interpersonal factors (i.e., family cohesion and social resources).
Method:
The sample consisted of 1,877 adolescents (946 girls) between 12 and 17 years old (Mage = 13.41, SD = 1.25) who completed self-report measures.
Results:
Approximately 11% of the participants had been involved in some type of self-harm on the internet. The prevalence was significantly higher among girls than boys and among adolescents older than 15 years old. Depression and anxiety increased the risk of self-harm on the internet, whereas family cohesion decreased the probability of self-harm on the internet.
Conclusions:
Self-harm on the internet is a relatively widespread phenomenon among Spanish adolescents. Prevention programs should include emotional regulation, coping skills, and resilience to reduce in this behavior.
German secondary education is known for its early, strict selection of students into different schooling tracks based on prior academic performance, based on the assumption that students learn more efficiently when the learning environment is tailored to their individual abilities and needs. While much previous research has shown that entry into tracks is socially selective, less is known whether there are effects of being exposed to a particular school track on educational success and which mechanisms are contributing to these effects. We investigate this question by comparing the learning progress in reading and mathematics of students in the upper and intermediate schooling track over five years of secondary schooling, based on large-scale German-wide longitudinal data (NEPS-SC3). Even when restricting our sample to a group of students with similar preconditions and controlling for skills at the beginning of secondary schooling, we find that the learning progress in the upper track is higher for both domains, suggesting scissor effects of track exposure. It is mainly the average performance level of the class, and to a lesser degree its social background composition, which mediates these effects. In contrast, migration background composition of the class and instructional quality perceived by students hardly contribute to explaining increasing learning gains in the upper track.