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Educational data mining is valuable for uncovering latent relationships in educational settings, particularly for predicting students' academic performance. This study introduces an interpretable hybrid model, optimised through Tree-structured Parzen Estimation (TPE) and Support Vector Regression (SVR), to predict overall scores (OT) utilising five assignments and one examination mark as predictors. Neural Network-based, Tree-Based, Ensemble-Based, and Boosting-based methods are evaluated against the hybrid TPE-optimised SVR model for forecasting final examination grades among 492 students enrolled in the TPP7155 (General Science) course at the University of Southern Queensland, Australia, during the 2020-2021 academic year. Additionally, Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanations (SHAP) techniques are employed to elucidate the inner workings of these prediction models. The findings highlight the superior performance of the proposed model, exhibiting the lowest Root Mean Squared Error (RMSE) and Relative Root Mean Squared Error (RRMSE), as well as the highest Willmott's index (WI), Legates–McCabe index (LM), and Nash–Sutcliffe Efficiency (NS). With assignment and examination marks identified as pivotal predictors of OT. SHAP and LIME analyses reveal the examination score (ET) as the most influential feature, impacting predicted OT by an average of ±4.93. Conversely, Assignment 1 emerges as the least informative feature, contributing merely ±0.64 to OT predictions. This research underscores the efficacy of the proposed interpretable hybrid TPE-optimised SVR model in discerning relationships among continuous learning variables, thereby empowering educators with early intervention capabilities and enhancing their ability to anticipate student performance prior to course completion.

BackgroundHypertension is one of the most important cardiovascular disease risk factors and affects >100 million American adults. Hypertension-related health inequities are abundant in Black communities as Black individuals are more likely to use the emergency department (ED) for chronic disease–related ambulatory care, which is strongly linked to lower blood pressure (BP) control, diminished awareness of hypertension, and adverse cardiovascular events. To reduce hypertension-related health disparities, we developed MI-BP, a culturally tailored multibehavior mobile health intervention that targeted behaviors of BP self-monitoring, physical activity, sodium intake, and medication adherence in Black individuals with uncontrolled hypertension recruited from ED and community-based settings. ObjectiveWe sought to determine the effect of MI-BP on BP as well as secondary outcomes of physical activity, sodium intake, medication adherence, and BP control compared to enhanced usual care control at 1-year follow-up. MethodsWe conducted a 1-year, 2-group randomized controlled trial of the MI-BP intervention compared to an enhanced usual care control group where participants aged 25 to 70 years received a BP cuff and hypertension-related educational materials. Participants were recruited from EDs and other community-based settings in Detroit, Michigan, where they were screened for initial eligibility and enrolled. Baseline data collection and randomization occurred approximately 2 and 4 weeks after enrollment to ensure that participants had uncontrolled hypertension and were willing to take part. Data collection visits occurred at 13, 26, 39, and 52 weeks. Outcomes of interest included BP (primary outcome) and physical activity, sodium intake, medication adherence, and BP control (secondary outcomes). ResultsWe obtained consent from and enrolled 869 participants in this study yet ultimately randomized 162 (18.6%) participants. At 1 year, compared to the baseline, both groups showed significant decreases in systolic BP (MI-BP group: 22.5 mm Hg decrease in average systolic BP and P

Wind, being a clean and sustainable resource, boasts environmental advantages. However, its electricity generation faces challenges due to unpredictable variations in wind speed (WS). Accurate predictions of these variations would allow mixed grids to adjust their energy mix in real-time, ensuring overall stability. For this purpose, the paper develops a new hybrid gated additive tree ensemble (H-GATE) model for accurate multi-step-ahead WS predictions. First, the multivariate empirical mode decomposition (MEMD) simultaneously demarcates the multivariate data into intrinsic mode functions (IMFs) and residuals. These components represent underlying trends, periodicity, and stochastic patterns in WS variations. The IMF and residual components are pooled in respective sets, and an opposition-based whale optimization algorithm (OBWOA) is applied for dimensionality reduction. The selected features are used by GATE tuned with Bayesian optimization (BO) to forecast the individual IMF and residual components. The outputs are summed to obtain the final multi-step-ahead WS forecasts. The proposed H-GATE is benchmarked against standalone (S-GATE, S-CLSTM, and S-ABR) and hybrid (H-CLSTM and H-ABR) models. Based on all statistical metrics and diagnostic plots, H-GATE outperforms all comparative models at all forecast horizons, accumulating the lowest mean absolute percentage error (MAPE) of 6.13 - 9.93% (at $t_{L+1}$ ), 8.67 - 14.07% (at $t_{L+2}$ ), and 11.60 - 18.37% (at $t_{L+3}$ ) across all three sites. This novel multi-step-ahead WS forecasting strategy can significantly benefit grid operators by helping anticipate fluctuations in wind power generation. This can assist in optimizing energy dispatch schedules, reducing reliance on backup power sources, and enhancing overall grid stability. Practical implementation of this method can help meet the rising energy demands through renewable wind energy.

Abstract Nothing is perfect and robots can make as many mistakes as any human, which can lead to a decrease in trust in them. However, it is possible, for robots to repair a human’s trust in them after they have made mistakes through various trust repair strategies such as apologies, denials, and promises. Presently, the efficacy of these trust repairs in the human–robot interaction literature has been mixed. One reason for this might be that humans have different perceptions of a robot’s mind. For example, some repairs may be more effective when humans believe that robots are capable of experiencing emotion. Likewise, other repairs might be more effective when humans believe robots possess intentionality. A key element that determines these beliefs is mind perception. Therefore understanding how mind perception impacts trust repair may be vital to understanding trust repair in human–robot interaction. To investigate this, we conducted a study involving 400 participants recruited via Amazon Mechanical Turk to determine whether mind perception influenced the effectiveness of three distinct repair strategies. The study employed an online platform where the robot and participant worked in a warehouse to pick and load 10 boxes. The robot made three mistakes over the course of the task and employed either a promise, denial, or apology after each mistake. Participants then rated their trust in the robot before and after it made the mistake. Results of this study indicated that overall, individual differences in mind perception are vital considerations when seeking to implement effective apologies and denials between humans and robots.

Explanations — reasons or justifications for action — are being used to promote the acceptance of automated vehicles (AVs). Yet, it is unclear whether and how the modality of explanation affects its effectiveness. Despite its importance in the technology acceptance literature, the impact of technology suspicion on the adoption of AVs is yet to be fully examined. To expand our understanding of AV explanation, we conducted a within-subjects experiment with 32 participants using a high-fidelity driving simulator. Four conditions were presented to participants: (1) auditory explanation with a non-driving-related task (NDRT), (2) auditory explanation without NDRT, (3) visual explanation with NDRT, and (4) visual explanation without NDRT. The results indicate that auditory explanations are more effective in reducing anxiety and unsafety perception for high-suspicion individuals, especially in the absence of NDRT. Conversely, individuals who are less technology suspicious prefer visual explanations, which can result in lower levels of anxiety and perceived unsafety. The study highlights the importance of considering individuals’ technology suspicion and engagement with NDRT when selecting the appropriate explanation modality, and the findings can guide the design of future AV systems to promote effective human-machine interaction.

Abstract Effective human–robot collaboration requires the appropriate allocation of indivisible tasks between humans and robots. A task allocation method that appropriately makes use of the unique capabilities of each agent (either a human or a robot) can improve team performance. This paper presents a novel task allocation method for heterogeneous human–robot teams based on artificial trust from a robot that can learn agent capabilities over time and allocate both existing and novel tasks. Tasks are allocated to the agent that maximizes the expected total reward. The expected total reward incorporates trust in the agent to successfully execute the task as well as the task reward and cost associated with using that agent for that task. Trust in an agent is computed from an artificial trust model, where trust is assessed along a capability dimension by comparing the belief in agent capabilities with the task requirements. An agent’s capabilities are represented by a belief distribution and learned using stochastic task outcomes. Our task allocation method was simulated for a human–robot dyad. The team total reward of our artificial trust-based task allocation method outperforms other methods both when the human’s capabilities are initially unknown and when the human’s capabilities belief distribution has converged to the human’s actual capabilities. Our task allocation method enables human–robot teams to maximize their joint performance.

Paid crowdsourcing connects task requesters to a globalized, skilled workforce that is available 24/7. In doing so, this new labor model promises not only to complete work faster and more efficiently than any previous approach but also to harness the best of our collective capacities. Nevertheless, for almost a decade now, crowdsourcing has been limited to addressing rather straightforward and simple tasks. Large-scale innovation, creativity, and wicked problem-solving are still largely out of the crowd’s reach. In this opinion paper, we argue that existing crowdsourcing practices bear significant resemblance to the management paradigm of Taylorism. Although criticized and often abandoned by modern organizations, Taylorism principles are prevalent in many crowdsourcing platforms, which employ practices such as the forceful decomposition of all tasks regardless of their knowledge nature and the disallowing of worker interactions, which diminish worker motivation and performance. We argue that a shift toward post-Taylorism is necessary to enable the crowd address at scale the complex problems that form the backbone of today’s knowledge economy. Drawing from recent literature, we highlight four design rules that can help make this shift, namely, endorsing social crowd networks, encouraging teamwork, scaffolding ownership of one’s work within the crowd, and leveraging algorithm-guided worker self-coordination.

For automated vehicles (AVs) to navigate safely, they must be able to anticipate and predict the behavior of pedestrians. This is particularly critical in urban driving environments where risks of collisions are high. However, a major challenge is that pedestrian behavior is inherently multimodal in nature, i.e., pedestrians can plausibly take multiple paths. This is because, in large part, pedestrian behaviors are driven by unique intentions and decisions made by each pedestrian walking along a particular sidewalk or crosswalk. As described in this paper, we developed a hybrid automaton model of multimodal pedestrian behavior called Multimodal Hybrid Pedestrian (MHP). We account for multimodal pedestrian behavior by identifying pedestrian decision-making points and developing decision-making models to predict pedestrian behaviors in a probabilistic hybrid automaton framework. The resulting MHP model is more likely to predict the ground truth trajectory compared to two baseline models-a baseline hybrid automaton model and a constant velocity model. The MHP model is applicable to a wide variety of urban scenarios-midblocks, intersections, one-way, and two-way streets, etc., and the probabilistic predictions from the model can be utilized for AV motion planning.

Automated vehicles (AV) have the potential to decrease driving-related accidents and traffic congestion and to reduce fuel consumption and carbon emissions. However, because of a lack of trust and acceptance, their widespread adoption is far from certain. One approach researchers have taken to promote trust and acceptance of AVs is to decrease the uncertainty associated with their actions by providing explanations. AV explanations are the reasons the AV provides to make its actions easier to understand. There is now a nascent but rapidly growing body of research on AV explanations. Yet, answers to basic questions like whether or when AV explanations are effective still elude us. To better understand what has been done and what should be done with regard to AV explanations, we present a review of the literature, discuss the findings and identify several important future research directions.

Since it was released on November 30, 2022, ChatGPT has offered numerous opportunities for higher education professors to improve their course offerings. However, not all information provided by the application is accurate. The application has been known to yield highly inaccurate information with high confidence. Yet, with that knowledge, ChatGPT can still save education practitioners hours of time and improve their courses, as well as help students with library research. By January, 2023, ChatGPT was the fastest growing consumer software application in history, with over 100 million users. Despite any misgivings, it is here to stay. This paper investigates how ChatGPT can be used help IS faculty more easily and effectively prepare and present their courses and discusses potential opportunities and threats from its use. Ways in which students may use the application for library research and feedback are also discussed. Generative AI in its current and evolving formats are proving helpful for students and faculty alike.

Individuals from diverse minority backgrounds in the United States represent a one-of-its-kind collaboration in understanding diversity, equity, inclusion, and belonging (DEIB) challenges in workforce staffing and representation per Equal Opportunity norms. This study involved primary and secondary research in the realm of DEI to develop an implementable model from lessons gleaned from the findings of the research and gaps found in contemporary hiring and staffing practices. The study and model development are grounded in Rawls's 1971 theory of justice and predicated on the premise that opportunities are usually based on merit, and are generally well-intended, although often constrained by the frailties of human nature. The United States is in dire need of practical solutions, for despite well-intended U.S. laws and mandates on employment, equitable hiring practices are far from satisfactory. In a changing world order, many employers in the United States, and even to some extent globally, acknowledge they are duty-bound to ensure a harmonious, equitable, and safe work environment, devoid of any form of discrimination, harassment, or intimidation. Although easier said than done, achieving this equality requires a systematic approach to investigative organizational/institutional auditing, mapping, and diagnosing the ills that constrain collaboration and reduce productivity. The model is based on research and strategic practitioner design in its orientation, with practical implementation utility, flexible and customizable specifically for DEIB initiatives change, and suitable for educational and other industry settings. A skilled and scientific approach is needed to ensure equitable practices in professional and academic spheres.

Background: In an earlier monocentric study, we have developed a novel non-invasive test system for the prediction of renal allograft rejection, based on the detection of a specific urine metabolite constellation. To further validate our results in a large real-world patient cohort, we designed a multicentric observational prospective study (PARASOL) including six independent European transplant centers. This article describes the study protocol and characteristics of recruited better patients as subjects.Methods: Within the PARASOL study, urine samples were taken from renal transplant recipients when kidney biopsies were performed. According to the Banff classification, urine samples were assigned to a case group (renal allograft rejection), a control group (normal renal histology), or an additional group (kidney damage other than rejection).Results: Between June 2017 and March 2020, 972 transplant recipients were included in the trial (1,230 urine samples and matched biopsies, respectively). Overall, 237 samples (19.3%) were assigned to the case group, 541 (44.0%) to the control group, and 452 (36.7%) samples to the additional group. About 65.9% were obtained from male patients, the mean age of transplant recipients participating in the study was 53.7 ± 13.8 years. The most frequently used immunosuppressive drugs were tacrolimus (92.8%), mycophenolate mofetil (88.0%), and steroids (79.3%). Antihypertensives and antidiabetics were used in 88.0 and 27.4% of the patients, respectively. Approximately 20.9% of patients showed the presence of circulating donor-specific anti-HLA IgG antibodies at time of biopsy. Most of the samples (51.1%) were collected within the first 6 months after transplantation, 48.0% were protocol biopsies, followed by event-driven (43.6%), and follow-up biopsies (8.5%). Over time the proportion of biopsies classified into the categories Banff 4 (T-cell-mediated rejection [TCMR]) and Banff 1 (normal tissue) decreased whereas Banff 2 (antibody-mediated rejection [ABMR]) and Banff 5I (mild interstitial fibrosis and tubular atrophy) increased to 84.2 and 74.5%, respectively, after 4 years post transplantation. Patients with rejection showed worse kidney function than patients without rejection.Conclusion: The clinical characteristics of subjects recruited indicate a patient cohort typical for routine renal transplantation all over Europe. A typical shift from T-cellular early rejections episodes to later antibody mediated allograft damage over time after renal transplantation further strengthens the usefulness of our cohort for the evaluation of novel biomarkers for allograft damage.

Robots have become vital to the delivery of health care and their personalities are often important to understanding their effectiveness as health care providers. Despite this, there is a lack of a systematic overarching understanding of personality in health care human-robot interaction. This makes it difficult to understand what we know and do not know about the impact of personality in health care human-robot interaction (H-HRI). As a result, our understanding of personality in H-HRI has not kept pace with the deployment of robots in various health care environments. To address this, the authors conducted a literature review that identified 18 studies on personality in H-HRI. This paper expands, refines, and further explicates the systematic review done in a conference proceedings [see: Esterwood (Proceedings of the 8th International Conference on Human-Agent Interaction, 2020, 87–95)]. Review results: 1) highlight major thematic research areas, 2) derive and present major conclusions from the literature, 3) identify gaps in the literature, and 4) offer guidance for future H-HRI researchers. Overall, this paper represents a reflection on the existing literature and provides an important starting point for future research on personality in H-HRI.

This paper proposes a Project-based team instruction methodology with open-ended projects to teach students critical analysis, design and implementation steps of developing Robotic Process Automation (RPA) for information systems. The use of project-based learning is appropriate for teaching RPA analysis and design with lean Six Sigma tools because of its experimental approach and documentation of logical steps needed to learn how to implement RPA successfully. The approach systematically documents work currently performed and defines future actions of the process while ensuring significant benefits are achieved with the RPA enhanced process. This methodology is important because the application of RPA is not commonly taught in Management Information System (MIS) programs. MIS students may not understand the significance of combined methodology, RPA tool, and usefulness of RPA until they enter the workforce where RPA is rapidly becoming available and easier to implement. The lecture sessions and exercises are valuable because it is easy to communicate the value of RPA in terms of time, quality, volume of transactions, etc. using Lean Six Sigma analytic approaches. The exercises involve hands on activities to make this learning experience interesting for students to readily associate the theoretical process improvement agreement and visualize the practical value of RPA enhanced projects. The paper discusses the need for process changes (and new development approaches) in organization to match the properties and functions within enterprise systems and ERPs that has led to criticism of the enterprise systems. This criticism is attributable to the ERPs' many subfunctions and operations that have limited adaptability and reduced functional and operational flexibility. The RPAs require limited prior knowledge of ERPs or their sub-processes for the improvements that are made in the performance of the organization. Thus, students do not have to "learn" how these enterprise or ERP systems operate to make changes or task improvements. This paper presents a project-based methodology and design approach focusing on development of RPAs that help students learning how to make the improvements using the RPS tools. The students learn that projects can deliver significant and tangible benefits to organizations while engaging students in key activities of the analysis, design and development process from a low code-no code perspective.

Medical training has become a global phenomenon, and the Physician's Charter (PC), as a missionary document, is key to training those outside the Global North. Undergraduate and postgraduate students in the medical profession are sometimes trained in contexts foreign to their social and ontological backgrounds. This might lead to confusion and blunders, creating an impression of what might look and feel unprofessional to those unfamiliar with the local context. Understanding the cultural backgrounds of the trainees is crucial, and the reverse is also as important. It is essential for clinicians and trainees to understand the cultural backgrounds of their patients to avoid miscommunication. In this phenomenological study, we recruited participants in 2020 who were in their first to fourth year of study of medical training during the #FeesMustFall protests. We used data from this extensive study looking at students' experiences during their training amidst protest and social upheavals in a South African tertiary institution. For this paper, we examined what professionalism means to the student participants using an African Ubuntu lens. Ubuntu and the Collective Finger theory were used to investigate what professionalism means to participants. The Ubuntu philosophy was compared to the PC. In the findings, the clinical space is hierarchical, silencing and the opposite of what Ubuntu means. In comparison to the PC, respect is overarching while compassion and responsibility are the most comparable to the Charter. This study adds an African voice to the professionalism discourse while showing African elements that could be aligned to the PC to challenge the current global discourses.

Purpose: The main objective of this study was to assess the existence of developmental effects on the performance of the Vocale Rapide dans le Bruit (VRB) speech-in-noise (SIN) identification test that was recently developed for the French language and to collect reference scores for children and adolescents. Method: Seventy-two native French speakers, aged 10-20 years, participated in the study. Each participant listened and repeated four lists of eight sentences, each containing three key words to be scored. The sentences were presented in free field at different signal-to-noise ratios (SNRs) using a four-talker babble noise. The SNR yielding 50% of correct repetitions of key words (SNR[subscript 50]) was recorded for each list. Results: A strong relationship between age and SNR[subscript 50] was found, better performance occurring with increasing age (average drop in SNR[subscript 50] per year: 0.34 dB). Large differences (Cohen's d [greater than or equal to] 1.2) were observed between the SNR[subscript 50] achieved by 10- to 13-year-old participants and those of adults. For participants aged 14-15 years, the difference fell just above the 5% level of significance. No effects of hearing thresholds or level of education were observed. Conclusions: The study confirms the existence of developmental effects on SIN identification performance as measured using the VRB test and provides reference data for taking into account these effects during clinical practice. Explanations as to why age effects perdure during adolescence are discussed.

Abstract Background Social accountability is defined as the responsibility of institutions to respond to the health priorities of a community. There is an international movement towards the education of health professionals who are accountable to communities. There is little evidence of how communities experience or articulate this accountability. Methods In this grounded theory study eight community based focus group discussions were conducted in rural and urban South Africa to explore community members’ perceptions of the social accountability of doctors. The discussions were conducted across one urban and two rural provinces. Group discussions were recorded and transcribed verbatim. Results Initial coding was done and three main themes emerged following data analysis: the consultation as a place of love and respect (participants have an expectation of care yet are often engaged with disregard); relationships of people and systems (participants reflect on their health priorities and the links with the social determinants of health) and Ubuntu as engagement of the community (reflected in their expectation of Ubuntu based relationships as well as part of the education system). These themes were related through a framework which integrates three levels of relationship: a central community of reciprocal relationships with the doctor-patient relationship as core; a level in which the systems of health and education interact and together with social determinants of health mediate the insertion of communities into a broader discourse. An ubuntu framing in which the tensions between vulnerability and power interact and reflect rights and responsibility. The space between these concepts is important for social accountability. Conclusion Social accountability has been a concept better articulated by academics and centralized agencies. Communities bring a richer dimension to social accountability through their understanding of being human and caring. This study also creates the connection between ubuntu and social accountability and their mutual transformative capacity as agents for social justice

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human–subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.