The perspectives of learners at a public medical school on the evaluation of an online learning management system for degree and non-degree courses

BACKGROUND

There has been a rapid development and adoption of online learning in medical education. However, it is difficult to adopt the currently available online learning management systems (LMS). This study aimed to examine learners' perspectives on the evaluation of online LMS.

METHODS

An online LMS was developed based on the evidence-based guidelines. Two cross-sectional studies were conducted. A short survey was conducted with 716 learners registered on the LMS to obtain their perspectives on the online participation. A satisfaction survey was conducted with 255 learners enrolled in the courses taught solely online. Data from the LMS monitoring system was used to report the uptake of online courses. Data were analyzed using descriptive statistics.

RESULTS

Participants reported that the major factor influencing LMS uptake was the ability to be accessed anytime and anywhere (n = 556, 77.7%). The participants had good experience in using the LMS and were satisfied with it (n = 255, mean = 4.53, SD = 0.62). For online degree courses, the course had a high completion rate of 90% provided that a mark was assigned for course attendance. For non-degree courses, irrespective of whether they were free, paid, exam-based, or participation only, the completion rate was considered low (range 4.3-36.7%).

CONCLUSION

Under a limited budget, a medical school in a low- to middle-income country could develop an effective online LMS to meet learners' needs. Our newly developed online LMS is relevant, accepted and to the satisfaction of the learners. Medical schools in the same context are encouraged to develop their own online LMS that serve and support learning in both degree and non-degree courses.

Challenge, integration, and change: ChatGPT and future anatomical education

With the vigorous development of ChatGPT and its application in the field of education, a new era of the collaborative development of human and artificial intelligence and the symbiosis of education has come. Integrating artificial intelligence (AI) into medical education has the potential to revolutionize it. Large language models, such as ChatGPT, can be used as virtual teaching aids to provide students with individualized and immediate medical knowledge, and conduct interactive simulation learning and detection. In this paper, we discuss the application of ChatGPT in anatomy teaching and its various application levels based on our own teaching experiences, and discuss the advantages and disadvantages of ChatGPT in anatomy teaching. ChatGPT increases student engagement and strengthens students' ability to learn independently. At the same time, ChatGPT faces many challenges and limitations in medical education. Medical educators must keep pace with the rapid changes in technology, taking into account ChatGPT's impact on curriculum design, assessment strategies and teaching methods. Discussing the application of ChatGPT in medical education, especially anatomy teaching, is helpful to the effective integration and application of artificial intelligence tools in medical education.

Cultivating physician empathy: a person-centered study based in self-determination theory

While physician empathy is a vital ingredient in both physician wellness and quality of patient care, consensus on its origins, and how to cultivate it, is still lacking. The present study examines this issue in a new and innovative way, through the lens of self-determination theory. Using survey methodology, we collected data from N = 177 (44%) students at a Canadian medical school. We then used a person-centered approach (cluster analysis) to identify medical student profiles of self-determination (based on trait autonomy and perceived competence in learning) and how the learning environment impacted empathy for those in each profile. When the learning environment was more autonomy-supportive, students experienced higher satisfaction and lower frustration of their basic psychological needs in medical school, as well as greater empathy towards patients. The translation into increased empathy, however, was only evident among the students with higher self-determination at baseline. Results from this study suggest that autonomy-supportive learning environments will generally support medical students' psychological needs for optimal motivation and well-being, but whether or not they lead to empathy towards patients will depend on individual differences in self-determination. Findings and their implications are discussed in terms of developing theory-driven approaches to cultivating empathy in medical education.

Risky business: medical students' feedback-seeking behaviours: a mixed methods study

There are differing views on how learners' feedback-seeking behaviours (FSB) develop during training. With globalisation has come medical student migration and programme internationalisation. Western-derived educational practices may prove challenging for diverse learner populations. Exploring undergraduate activity using a model of FSB may give insight into how FSB evolves and the influence of situational factors, such as nationality and site of study. Our findings seek to inform medical school processes that support feedback literacy. Using a mixed methods approach, we collected questionnaire and interview data from final-year medical students in Ireland, Bahrain, and Malaysia. A validated questionnaire investigated relationships with FSB and goal orientation, leadership style preference, and perceived costs and benefits. Interviews with the same student population explored their FSB experiences in clinical practice, qualitatively, enriching this data. The data were integrated using the 'following the thread' technique. Three hundred and twenty-five of a total of 514 completed questionnaires and 57 interviews were analysed. Learning goal orientation (LGO), instrumental leadership and supportive leadership related positively to perceived feedback benefits (0.23, 0.2, and 0.31, respectively, p < 0.05). Perceived feedback benefits are related positively to feedback monitoring and inquiry (0.13 and 0.38, respectively, p < 0.05). The personal cost of feedback is unsupported in quantitative data, but was a strong theme in interviews, as was feedback avoidance, peer feedback, and unsupportive learning environment. No differences were observed across sub-groups based on gender, study site, or student nationality. Integrated analysis describes FSB: avoiding 'unsafe' feedback (first, do no harm) and overcoming barriers (beat the system) and goal-centred curation (shop around) to optimise benefits. Diverse medical students across three continents undertake FSB with careful navigation, as a valued but risky business, that is highly contextualised. Promoting a constructive FSB is complex. Overcoming outdated theory and practices on the wards remains a challenge to psychologically safe, learner-centred feedback.

Minding the gap: towards a shared clinical reasoning lexicon across the pre-clerkship/clerkship transition

Teaching and learning of clinical reasoning are core principles of medical education. However, little guidance exists for faculty leaders to navigate curricular transitions between pre-clerkship and clerkship curricular phases. This study compares how educational leaders in these two phases understand clinical reasoning instruction. Previously reported cross-sectional surveys of pre-clerkship clinical skills course directors, and clerkship leaders were compared. Comparisons focused on perceived importance of a number of core clinical reasoning concepts, barriers to clinical reasoning instruction, level of familiarity across the undergraduate medical curriculum, and inclusion of clinical reasoning instruction in each area of the curriculum. Analyses were performed using the Mann Whitney U test. Both sets of leaders rated lack of curricular time as the largest barrier to teaching clinical reasoning. Clerkship leaders also noted a lack of faculty with skills to teach clinical reasoning concepts as a significant barrier (p < 0.02), while pre-clerkship leaders were more likely to perceive that these concepts were too advanced for their students (p < 0.001). Pre-clerkship leaders reported a higher level of familiarity with the clerkship curriculum than clerkship leaders reported of the pre-clerkship curriculum (p < 0.001). As faculty transition students from the pre-clerkship to the clerkship phase, a shared understanding of what is taught and when, accompanied by successful faculty development, may aid the development of longitudinal, milestone-based clinical reasoning instruction.

Unpacking the perceptions and experiences of student facilitators in interprofessional education: a qualitative study

Enhancing health professional students' effective learning and collaborative practice requires a deep understanding of strategies for facilitating interprofessional learning. While faculty members and clinical preceptors are recognized as facilitators in interprofessional education (IPE), there is limited knowledge about the impact of student facilitators' engagement in IPE. Accordingly, this study aims to explore the perceptions and experiences of student facilitators in IPE. Thirteen student facilitators were recruited to lead an interprofessional learning program, and they were subsequently invited to participate in one-on-one interviews. An interview guide was developed to explore their motivations, expectations, engagement, effectiveness, and achievements in IPE facilitation. Thematic analysis was conducted using MAXQDA software to analyze the student facilitators' experiences and perceptions. Eight interviewees from various disciplines, including Medicine, Nursing, Pharmacy, Speech and Hearing Sciences, and Social Work, took part in the study. The findings revealed that student facilitators highly valued their IPE facilitation experience, which aligned with their expectations and led to the creation of social networks, increased confidence, improved understanding of other professions, and the development of lifelong skills. Furthermore, the student facilitators demonstrated cognitive and social congruence by establishing a relaxed learning environment, displaying empathetic and supportive behaviors, and using inclusive language to engage IPE learners in group discussions. This study provides a comprehensive understanding of the role of student facilitators in IPE, contributing to the evolving literature on IPE. A conceptual framework was developed to explore the entire facilitation experience, encompassing the motivations and expectations of student facilitators, their engagement and effectiveness, and the observed achievements. These findings can inform the development of peer teaching training in IPE and stimulate further research in identifying relevant facilitator competencies for optimal delivery of IPE.

Addressing unprofessional behaviors in the clinical learning environment: lessons from a multi-year virtual, intergenerational, interdisciplinary workshop

INTRODUCTION

Optimizing the clinical learning environment (CLE) is a medical education priority nationwide.

MATERIALS AND METHODS

We developed a virtual, one-hour workshop engaging students, housestaff and faculty in small-group discussions of five case scenarios adapted from reported unprofessional behaviors in the CLE, plus didactics regarding mistreatment, microaggressions and bystander interventions.

RESULTS

Over two sessions (2021-2022), we engaged 340 students and 73 faculty/housestaff facilitators. Post-session surveys showed significant improvement in participants' ability to recognize and respond to challenges in the CLE.

DISCUSSION

Our innovative workshop, including scenarios derived from institutional reports of unprofessional behaviors, advanced participants' knowledge and commitment to improve the CLE.

Games to support teaching clinical reasoning in health professions education: a scoping review

INTRODUCTION

Given the complexity of teaching clinical reasoning to (future) healthcare professionals, the utilization of serious games has become popular for supporting clinical reasoning education. This scoping review outlines games designed to support teaching clinical reasoning in health professions education, with a specific emphasis on their alignment with the 8-step clinical reasoning cycle and the reflective practice framework, fundamental for effective learning.

METHODS

A scoping review using systematic searches across seven databases (PubMed, CINAHL, ERIC, PsycINFO, Scopus, Web of Science, and Embase) was conducted. Game characteristics, technical requirements, and incorporation of clinical reasoning cycle steps were analyzed. Additional game information was obtained from the authors.

RESULTS

Nineteen unique games emerged, primarily simulation and escape room genres. Most games incorporated the following clinical reasoning steps: patient consideration (step 1), cue collection (step 2), intervention (step 6), and outcome evaluation (step 7). Processing information (step 3) and understanding the patient's problem (step 4) were less prevalent, while goal setting (step 5) and reflection (step 8) were least integrated.

CONCLUSION

All serious games reviewed show potential for improving clinical reasoning skills, but thoughtful alignment with learning objectives and contextual factors is vital. While this study aids health professions educators in understanding how games may support teaching of clinical reasoning, further research is needed to optimize their effective use in education. Notably, most games lack explicit incorporation of all clinical reasoning cycle steps, especially reflection, limiting its role in reflective practice. Hence, we recommend prioritizing a systematic clinical reasoning model with explicit reflective steps when using serious games for teaching clinical reasoning.

Developing a framework for promoting interest and engagement of scholarship of teaching and learning for medical students

BACKGROUND

The scholarship of teaching and learning (SoTL) is a field of academic research that focuses on improving learning through reflective and informed teaching. Currently, most SoTL-related work is faculty-driven; however, student involvement in SoTL has been shown to benefit both learners and educators. Our study aims to develop a framework for increasing medical students' interest, confidence, and engagement in SoTL.

METHODS

A student-led SoTL interest group was developed and a year-round program of SoTL was designed and delivered by student leaders of the group under the guidance of a faculty advisor. Individual post-session surveys were administered to evaluate participants' perceptions of each session. Pre- and post-program surveys were administered to evaluate the program impact.

RESULTS

The year-round SoTL program consistently attracted the participation of medical students and faculty. Survey responses indicated strong medical student interest in the program and positive impact of the program. Increased interest and confidence in medical education research were reported by the student participants. The program design provided opportunities for student participants to network and receive ongoing feedback about medical education research they were interested or involved in.

CONCLUSION

Our study provides insights for developing a framework that other institutions can reference and build upon to educate and engage students in SoTL.

Embedding research capacity strengthening in multi-country studies in low-and middle-income countries: learnings from sexual and reproductive health research

Research capacity strengthening (RCS) can empower individuals, institutions, networks, or countries to define and prioritize problems systematically; develop and scientifically evaluate appropriate solutions; and reinforce or improve capacities to translate knowledge into policy and practice. However, how to embed RCS into multi-country studies focusing on sexual and reproductive health and rights (SRHR) is largely undocumented. We used findings from a qualitative study, from a review of the literature, and from a validation exercise from a panel of experts from research institutions that work on SRHR RCS. We provide a framework for embedded RCS; suggest a set of seven concrete actions that research project planners, designers, implementers, and funders can utilise to guide embedded RCS activities in low- and middle-income countries; and present a practical checklist for planning and assessing embedded RCS in research projects.

From static web to metaverse: reinventing medical education in the post-pandemic era

The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes.

Sex-specific modulation of safety learning in Shank2-deficient mice

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impaired perceptual processing and social communication, intellectual disabilities, and repetitive behaviors. Interestingly, while not a core symptom, anxiety disorders frequently co-occur in individuals with ASD and deficits in safety learning have been described in patients with anxiety-related disorders. Because genetic factors, such as SHANK deficiency (loss-of-function mutations), have been linked to ASD, the aim of the present study was to investigate whether Shank2 deficiency interferes with associative fear and safety signal learning. To first investigate trait anxiety, male and female Shank2-deficient mice were exposed to a light-dark box test. Mice were then submitted to a combination of contextual fear conditioning and single-cue safety conditioning. The results show that Shank2 deficiency increases trait anxiety but reduces contextual fear learning. In male but not female Shank2-deficient mice, reduced single-cued safety learning was observed. This safety learning deficit was not caused by altered anxiety levels, increased locomotor activity, or reduced contextual fear since these changes were also observed in female Shank2-deficient mice. Concluding, our data indicate that the observed safety learning deficits in Shank2-deficient male mice could contribute to the emotional symptoms observed in ASD and the high comorbidity with anxiety-related disorders.

The effectiveness of continuing professional development for the residential long-term care workforce: A systematic review

OBJECTIVES

To investigate the effectiveness of continuing professional development in residential long-term care.

DESIGN

Systematic review.

DATA SOURCES

PubMed, Cumulative Index to Nursing & Allied Health (CINAHL), and Web of Science.

REVIEW METHODS

Empirical studies published between 2003 and 2023 describing the effectiveness of continuing professional development in long-term care were selected according to PRISMA guidelines. The type, topic, and effectiveness of continuing professional development activities in long-term care were analysed, in addition to facilitators and barriers. The protocol of this review is registered in PROSPERO.

RESULTS

A total of 155 studies were selected, including over 17,000 participants the majority of whom were nurses. The most common topics were 'dementia care' (n = 22; 14.2 %), and restraint use (n = 14; 9 %). The impact of continuing professional development was mainly evaluated in terms of 'participant satisfaction with continuing professional development' (n = 5; 3 %), 'staff knowledge' (n = 57; 37 %), 'staff competencies and skills' (n = 35; 23 %), 'resident outcomes' (n = 45; 29 %), and 'staff wellbeing' (n = 12; 8 %). A total of 64 (41 %) studies evaluated if impact of continuing professional development was sustained over time. 'Good organisation', 'a supportive learning environment', 'expressing personal preferences', and 'management support' were described as facilitators of continuing professional development.

CONCLUSIONS

Increasing numbers of long-term care residents with complex health conditions require nurses with advanced skills, such as dementia care. To improve the effectiveness of continuing professional development, support from managers, who adopt relational leadership styles, is instrumental to integrate new knowledge and skills into practice. This needs to be linked to career progression, and consequently increase the attractiveness of working in the long-term care sector. This could meet the dual goal of improving outcomes for residents and nurses' job satisfaction.

Missed nursing care among nursing students: A scoping review

BACKGROUND

Nursing students report facing inconsistencies between theoretical knowledge and actualities of clinical practice, and research indicates they witness missed nursing care during clinical practice. Understanding nursing students' experiences can inform improved clinical learning environments and enhance patient care quality. Recently, research has focused on students' views regarding missed care, yet comprehensive understandings of this phenomenon are lacking.

OBJECTIVE

This review explored what is known about missed nursing care from perspectives of nursing students.

DESIGN

A scoping review according to Arksey and O'Malley's framework and Joanna Briggs Institute methodology was undertaken.

DATA SOURCES

Key databases were searched, including Medline, Embase, CINAHL, Scopus, ProQuest, PsycINFO and Web of Science in August 2023.

REVIEW METHODS

The review included qualitative, quantitative, and structured literature reviews published in English between 2006 and 2023. Inclusion and exclusion criteria and keywords guided the search. Data screening and extraction were independently performed by two reviewers. Included studies were analysed using descriptive statistics and narrative synthesis. Findings are reported in accordance with the PRISMA-ScR.

RESULTS

Of the 5873 articles identified, nine were included in the review. Studies were conducted in seven countries. Themes in students' perceptions included negligence of patients' rights and dignity, living in limbo, and pragmatic acceptance. Reasons for missed care included ineffective teamwork, lack of interest in caring, and inefficient leadership. Missed nursing care adversely affected patient health, hindered student learning and professional growth. Strategies for improvement included enhancing teamwork, enforcing ethical issues, increasing supervision, and creating commitment to work.

CONCLUSION

Missed nursing care affects patient safety and education of nursing students. Incorporating practical training modules into curricula, establishing preceptorship programs, and incorporating ethics education into clinical practice are crucial to address missed nursing care. Further research in diverse settings is warranted.

Fear extinction is impaired in aged rats

Normal aging is accompanied by broad loss of cognitive function in humans and rodents, including declines in cognitive flexibility. In extinction, a conditional stimulus (CS) that was previously paired with a footshock is presented alone. This procedure reliably reduces conditional freezing behavior in young adult rats. Here, we aimed to investigate how normal aging affects extinction learning. Using young (3 months) and aged (20 months) male and female Long Evans rats, we compared extinction (using 20 CS-alone presentations) to a no extinction control (equal exposure to the conditioning chamber without CS presentations) following delay fear conditioning. We found that young animals in the extinction group showed a decrease in freezing following extinction; aged animals did not. We next examined changes in neural activity using expression of the immediate early gene zif268. In young animals, extinction corresponded with decreased expression of zif268 in the basolateral amygdala and anterior retrosplenial cortex; this was not observed in aged animals. Further, aged animals showed increased zif268 expression in each region examined, suggesting that dysfunction in neural activity precedes cognitive deficits. These results demonstrate that aging impacts both extinction learning and neural activity.

Adaptive selection of local and non-local attention mechanisms for speech enhancement

In speech enhancement tasks, local and non-local attention mechanisms have been significantly improved and well studied. However, a natural speech signal contains many dynamic and fast-changing acoustic features, and focusing on one type of attention mechanism (local or non-local) cannot precisely capture the most discriminative information for estimating target speech from background interference. To address this issue, we introduce an adaptive selection network to dynamically select an appropriate route that determines whether to use the attention mechanisms and which to use for the task. We train the adaptive selection network using reinforcement learning with a developed difficulty-adjusted reward that is related to the performance, complexity, and difficulty of target speech estimation from the noisy mixtures. Consequently, we propose an Attention Selection Speech Enhancement Network (ASSENet) with the innovative dynamic block that consists of an adaptive selection network and a local and non-local attention based speech enhancement network. In particular, the ASSENet incorporates both local and non-local attention and develops the attention mechanism selection technique to explore the appropriate route of local and non-local attention mechanisms for speech enhancement tasks. The results show that our method achieves comparable and superior performance to existing approaches with attractive computational costs.

Structure-aware contrastive hashing for unsupervised cross-modal retrieval

Cross-modal hashing has attracted a lot of attention and achieved remarkable success in large-scale cross-media similarity retrieval applications because of its superior computational efficiency and low storage overhead. However, constructing similarity relationship among samples in cross-modal unsupervised hashing is challenging because of the lack of manual annotation. Most existing unsupervised methods directly use the representations extracted from the backbone of their respective modality to construct instance similarity matrices, leading to inaccurate similarity matrices and resulting in suboptimal hash codes. To address this issue, a novel unsupervised hashing model, named Structure-aware Contrastive Hashing for Unsupervised Cross-modal Retrieval (SACH), is proposed in this paper. Specifically, we concurrently employ both high-dimensional representations and discriminative representations learned by the network to construct a more informative semantic correlative matrix across modalities. Moreover, we design a multimodal structure-aware alignment network to minimize heterogeneous gap in the high-order semantic space of each modality, effectively reducing disparities within heterogeneous data sources and enhancing the consistency of semantic information across modalities. Extensive experimental results on two widely utilized datasets demonstrate the superiority of our proposed SACH method in cross-modal retrieval tasks over existing state-of-the-art methods.

A comparison of two learning approach inventories and their utility in predicting examination performance and study habits

The revised two-factor Study Process Questionnaire and the Approaches and Study Skills Inventory for Students are two instruments commonly used to measure student learning approach. Although they are designed to measure similar constructs, it is unclear whether the metrics they provide differ in terms of their real-world classification of learning approach. The purpose of this study is to compare outcomes of these two inventories in a study population from an undergraduate (baccalaureate) human anatomy course. The three central goals of this study are to compare the inventories in terms of 1) how students are classified, 2) the relationship between examination performance, time spent studying, and learning approach, and 3) instrument reliability. Results demonstrate that student classifications of corresponding scales of each inventory are significantly correlated, suggesting they measure similar constructs. Although the inventories had similar reliability, neither was consistently strong in predicting examination performance or study habits. Overall, these results suggest that the two inventories are comparable in terms of how they measure learning approach, but the lack of correspondence between learning approach scores and measurement outcomes questions their validity as tools that can be used universally in classrooms.NEW & NOTEWORTHY Although learning approach inventories have been used extensively in education research, there has been no direct comparison of how student classification differs between instruments or how classification influences the interpretation of how learning approach impacts student performance. This is especially relevant in light of recent research questioning the validity of the Study Process Questionnaire (LoGiudice AB, Norman GR, Manzoor S, Monteiro S. Adv Health Sci Educ Theory Pract 28: 47-63, 2023; Johnson SN, Gallagher ED, Vagnozzi AM. PLoS One 16: e0250600, 2021).

Effectiveness of exposure-based treatment for childhood anxiety disorders: An open clinical trial to test its relation with indices of emotional processing and inhibitory learning

BACKGROUND AND OBJECTIVES

The current study examined how effectiveness of exposure-based CBT was related to indices of emotional processing and inhibitory learning during exposure exercises.

METHODS

Adolescents with anxiety disorder(s) (N = 72; age 11-19; 85% girls) received a group-based, intensive two-week treatment of which effectiveness was indexed by the SCARED and by ratings of anxiety and approach towards individualized goal situations. To index emotional processing, subjective units of distress (SUDs) were used to indicate both initial and final fear level, and absolute, relative, and total dose of fear reduction. To index inhibitory learning, subjective threat expectancies (STEs) were used to indicate initial and final threat expectancy, and absolute, relative, and total dose of expectancy change.

RESULTS

From pre-treatment to follow-up, there was a large-sized reduction of anxiety symptoms, small-sized decrease of subjective anxiety and a large-sized increase in subjective approach towards individual treatment goals. Higher fear levels prior to exposure were related to a larger decrease of symptoms. Higher threat expectancies after exposure exercises were independently associated with less decrease of anxiety and increase of approach towards treatment goals. Total dose of experienced fear reduction and total dose of experienced expectancy change were (partly) independently related to more increase in approach towards individualized goal situations.

LIMITATIONS

As patients also received other treatment elements, the results cannot be interpreted unequivocally.

CONCLUSIONS

The pattern of findings seems to indicate that emotional processing (as indexed by fear reduction) and inhibitory learning (as indexed by expectancy change) are both relevant in exposure-based CBT.

MuLAN: Multi-level attention-enhanced matching network for few-shot knowledge graph completion

Recent years have witnessed increasing interest in the few-shot knowledge graph completion due to its potential to augment the coverage of few-shot relations in knowledge graphs. Existing methods often use the one-hop neighbors of the entity to enhance its embedding and match the query instance and support set at the instance level. However, such methods cannot handle inter-neighbor interaction, local entity matching and the varying significance of feature dimensions. To bridge this gap, we propose the Multi-Level Attention-enhanced matching Network (MuLAN) for few-shot knowledge graph completion. In MuLAN, a multi-head self-attention neighbor encoder is designed to capture the inter-neighbor interaction and learn the entity embeddings. Then, entity-level attention and instance-level attention are responsible for matching the query instance and support set from the local and global perspectives, respectively, while feature-level attention is utilized to calculate the weights of the feature dimensions. Furthermore, we design a consistency constraint to ensure the support instance embeddings are close to each other. Extensive experiments based on two well-known datasets (i.e., NELL-One and Wiki-One) demonstrate significant advantages of MuLAN over 11 state-of-the-art competitors. Compared to the best-performing baseline, MuLAN achieves 14.5% higher MRR and 13.3% higher Hits@K on average.

Assessing the benefits of an online prematriculation anatomy workshop on knowledge acquisition and anatomy competency in a Doctor of Physical Therapy program

Preparing students for the transition to graduate-level education with greater learning demands in a condensed time frame is a challenging process for health professions educators and incoming students. Prematriculation programs offer a solution for exposing students to the foundational sciences in preparation for the academic rigor of a doctoral program. This retrospective study assessed whether incoming students enrolled across 3 yr of a 2-day online anatomy workshop, offered in July and August before the start of their first semester resulted in improved anatomy knowledge. Whether this acquired knowledge translated to improved anatomy outcomes in the first semester of a Doctor of Physical Therapy program was also assessed, while also accounting for variables of gender, ethnicity, and grade-point average. Knowledge acquired during both days of the workshop resulted in statistically significant improvements in anatomy postquiz scores compared to the baseline prequiz (P < 0.001). Multivariate regression analyses demonstrated statistically significant relationships between the first-semester anatomy practical score and workshop participation (P = 0.04) as well as a predictive value of gender (P = 0.01). Evaluating a timing effect on the predictive value of the online anatomy workshop demonstrated statistically significant effects of the prematriculation workshop on both first-semester anatomy practicals for August (P = 0.03 for practical 1; P = 0.04 for practical 2) but not July workshop participants. Findings from this study support the utility of an online prematriculation anatomy workshop to prepare students for graduate-level anatomy learning expectations in a doctoral allied health program.NEW & NOTEWORTHY This paper presents the findings of a retrospective study examining the effectiveness of an online prematriculation anatomy workshop on knowledge acquisition and first-semester anatomy competency following the success of a previously offered peer-led onsite workshop. To our knowledge, this is the first report of an online prematriculation program that successfully introduces graduate-level learning expectations and access to anatomical resources leading to improved anatomy competency in an allied health professional program.

"Aquilibria: The battle to balance"-a narrative card and board game on acid-base regulation for first-year medical students

Recognizing the growing value of game-based learning in medical education, this study aims to evaluate the effectiveness of the innovation, "Aquilibria: The Battle to Balance," a creative narrative card and board game to help improve learners' understanding and application of the concepts of acid-base balance. In this mixed-method study, 120 first-year medical students participated. The innovation employed a card and board style integrated with a captivating story. Students were divided into small groups of six each with a facilitator. Following this, the posttest was conducted to compare the educational gain. Also, students' perceptions about the game were obtained using a 32-item questionnaire on a 5-point Likert scale. In addition to this, the confidence level among students to understand and interpret the concepts of acid-base regulation before and after the game was obtained using a 10-item questionnaire on a 5-point Likert scale. Furthermore, for qualitative data, short interviews with open-ended questions were conducted and thematic analysis was performed. The results showed a highly significant improvement in academic performance from a pretest score of 7.57 ± 1.07 (means ± SD) to 16.14 ± 1.80 in the posttest with a P value of <0.0001. There was a notable increase in confidence among learners after the game, and highly positive student feedback was received. These findings support the growing recognition of narrative game-based learning as a valuable and engaging strategy in medical education, offering a promising avenue for fostering deeper understanding and retention of complex medical concepts.NEW & NOTEWORTHY This innovation is a captivating blend of storytelling, cards, and board gameplay to facilitate the learning of acid-base regulation. This engaging game offers a wealth of questions and diverse case scenarios, allowing learners to repeatedly explore and grasp the intricacies of acid-base balance. What sets this game apart is its robust assessment strategy, supported by overwhelmingly positive feedback and marked academic improvement. This innovation is a must-have for students seeking a dynamic and effective learning experience.

Spoken verb learning in children with language disorder

The current study examined spoken verb learning in elementary school children with language disorder (LD). We aimed to replicate verb learning deficits reported in younger children with LD and to examine whether verb instrumentality, a semantic factor reflecting whether an action requires an instrument (e.g., "to chop" is an instrumental verb), influenced verb learning. The possible facilitating effect of orthographic cues presented during training was also evaluated. In an exploratory analysis, we investigated whether language and reading skills mediated verb learning performance. General language skills and verb learning were assessed in Dutch children with LD and age-matched typically developing controls (n = 25 per group) aged 8 to 12 years (M = 9;9 [years;months], SD = 1;3). Using video animations, children learned 20 nonwords depicting actions comprising 10 instrumental and 10 noninstrumental verbs. Half of the items were trained with orthographic information present. Verb learning was assessed using an animation-word matching and animation naming task. Linear mixed-effects models showed a main effect of group for all verb learning measures, demonstrating that children with LD learned fewer words and at a slower rate than the control group. No effect of verb instrumentality, presence of orthographic information, or the included mediators was found. Our results emphasize the importance of continued vocabulary instruction in elementary school to strengthen verb encoding. Given that our findings are inconsistent with the overall literature showing an orthographic facilitation effect, future studies should investigate whether participants pay attention to the written word form in learning contexts with moving stimuli.

Infants' top-down perceptual modulation is specific to own-race faces

Recent studies have revealed the influence of higher-level cognitive systems in modulating perceptual processing (top-down perceptual modulation) in infancy. However, more research is needed to understand how top-down processes in infant perception contribute to early perceptual development. To this end, this study examined infants' top-down perception of own- and other-race faces to reveal whether top-down modulation is linked to the emergence of perceptual specialization. Infants first learned an association between a sound and faces, with the race of the faces manipulated between groups (own race vs. other race). We then tested infants' face perception across various levels of perceptual difficulty (manipulated by presentation duration) and indexed top-down perception by the change in perception when infants heard the sound previously associated with the face (predictive sound) versus an irrelevant sound. Infants exhibited top-down face perception for own-race faces (Experiment 1). However, we present new evidence that infants did not show evidence of top-down modulation for other-race faces (Experiment 2), suggesting an experience-based specificity of this capacity with more effective top-down modulation in familiar perceptual contexts. In addition, we ruled out the possibility that this face race effect was due to differences in infants' associative learning of the sound and faces between the two groups. This work has important implications for understanding the mechanisms supporting perceptual development and how they relate to top-down perception in infancy.

Perspectives on self-directed learning in bioscience in nurse education - An integrative review

OBJECTIVES

There is a lack of synthesized knowledge on nursing students self-directed learning in bioscience and how to best support students' learning in this subject. The purpose of this integrative review is to synthesize current literature on perspectives on self-directed learning among nursing students studying bioscience to guide further research aiming to support students' learning more effectively.

METHODS

An integrative review in line with Whittemore & Knafl's modified framework containing five stages: problem identification, literature search, data evaluation, data analysis and presentation. A structured literature search was undertaken in the Web of Science, ERIC, Medline and CINAHL databases from November 2022 to January 2023. The inclusion criteria were nursing students enrolled in a bachelor programme, research addressing activities intended for learning bioscience, in addition to formal taught lectures and perspectives on self-directed learning in natural science subjects within nurse education such as views, actions, activities, habits and attitudes. Exclusion criteria were students in other education programs, research in formal learning contexts, and self-directed learning in other subjects than natural science subjects. Rigour of each included source was assessed using Whittemore & Knafl's suggested 2-point scale (high or low). A constant comparison method was used to synthesize results.

RESULTS

Of the initial 1143 sources, 12 articles were included after abstract and full-text screening: one pilot study for randomized controlled trial, one qualitative study, two mixed methods studies and eight quantitative studies. The sample size was from 23 to 563 participants.

DISCUSSION

This review identifies self-directed learning in bioscience understood as a continuum of teacher-directedness and self-directedness rather than as distinguished orientations. There seem to be no consistent definition of self-directed learning in bioscience, yet descriptions commonly imply metacognitive learning approaches. Nursing students value digital learning resources, yet technology might be secondary to the skill of self-directed learning.

Effects of an unfolding case study on clinical reasoning, self-directed learning, and team collaboration of undergraduate nursing students: A mixed methods study

BACKGROUND

Clinical reasoning is an essential nursing competency that students must develop to provide safe patient care. Developing and utilizing unfolding case studies, which present constantly changing patient conditions to improve students' clinical reasoning and to foster communication and self-reflection, can help to achieve that imperative.

OBJECTIVES

To develop an unfolding case study and to test its effectiveness in improving clinical reasoning, team collaboration, and self-directed learning.

DESIGN

A mixed methods design.

SETTING

One university in Southern Taiwan.

PARTICIPANTS

Forty nursing students.

METHODS

An unfolding case study was developed based on the clinical reasoning model and unfolding cases model. The Nurses Clinical Reasoning Scale, Self-Directed Learning Instrument, and Questionnaire of Group Responsibility and Cooperation in Learning Teams were used. Forty nursing students completed questionnaires and nine of them participated in focus group discussions. Wilcoxon signed-rank, Spearman correlation, regression, and inductive content analysis were used to analyze data.

RESULTS

Students' abilities in clinical reasoning, self-directed learning, and team collaboration were statistically significantly improved after implementation of the unfolding case study. Emergent themes included "patient-centered communication," "group inspiration and learning," "thinking critically and reflecting on oneself," and "applying theoretical knowledge in care to meet patients' changing needs."

CONCLUSIONS

Unfolding case studies provide a safe environment in which nursing students may learn and apply knowledge to safe patient care.

Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi-order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi-supervised classification performance compared with state-of-the-art competitors.

CTF-former: A novel simplified multi-task learning strategy for simultaneous multivariate chaotic time series prediction

Multivariate chaotic time series prediction is a challenging task, especially when multiple variables are predicted simultaneously. For multiple related prediction tasks typically require multiple models, however, multiple models are difficult to keep synchronization, making immediate communication between predicted values challenging. Although multi-task learning can be applied to this problem, the principles of allocation and layout options between shared and specific representations are ambiguous. To address this issue, a novel simplified multi-task learning method was proposed for the precise implementation of simultaneous multiple chaotic time series prediction tasks. The scheme proposed consists of a cross-convolution operator designed to capture variable correlations and sequence correlations, and an attention module proposed to capture the information embedded in the sequence structure. In the attention module, a non-linear transformation was implemented with convolution, and its local receptive field and the global dependency of the attention mechanism achieve complementarity. In addition, an attention weight calculation was devised that takes into account not only the synergy of time and frequency domain features, but also the fusion of series and channel information. Notably the scheme proposed a purely simplified design principle of multi-task learning by reducing the specific network to single neuron. The precision of the proposed solution and its potential for engineering applications were verified with the Lorenz system and power consumption. The mean absolute error of the proposed method was reduced by an average of 82.9% in the Lorenz system and 19.83% in power consumption compared to the Gated Recurrent Unit.

Long-term impact of neuroscience outreach interventions on elementary students' knowledge

Neuroeducation is characterized as a subarea of neuroscience that involves comprehending the teaching and learning processes and relating them to neuroanatomy, neurophysiology, and neuropsychology. The inclusion of some aspects of the neuroscience of learning in teachers' and students' formation, applying them in teaching-learning environments, contributes to the quality of education and impacts students' quality of life and health. Thus, the POPNEURO outreach program performs interventions with students and teachers of low-income schools to disseminate neuroscience concepts, relating them to the students' daily lives. This study reports the impact of these actions, assessed 1 yr after their conclusion. The results showed that the long-term impact of the activities carried out is, in general, positive. Even 1 yr after the activities end, students demonstrate knowledge about the neuroscience themes and satisfaction with participating.NEW & NOTEWORTHY This article reports on neuroscience disclosure activities performed with school students and describes their short- and long-term positive impact. Even 1 yr after the activities, students demonstrate knowledge about the themes worked on and satisfaction with the activities.

Using artificial intelligence platforms to support student learning in physiology

The advent of artificial intelligence (AI)-powered language models presents new opportunities and challenges in education. By teaching students how to craft prompts that elicit insightful responses, faculty can scaffold activities where AI acts as a supplemental resource to amplify critical thinking and support student learning. Ongoing dialogue and iteration focused on ethical usage norms can achieve the right balance between emerging technology and foundational skills development. With care and intention, AI-assisted study tactics offer students personalized support while adhering to academic standards. While AI-powered tools provide many positive opportunities, students and faculty need to learn about and use them responsibly and ethically, not as replacements for required thinking and effort. Before implementing these AI tools for studying biology, there are several key things to discuss with students. This article outlines several ways that students can employ these tools to support better learning along with a set of guidelines for all to be wary of when implementing these in an academic setting.NEW & NOTEWORTHY Utilizing of artificial intelligence tools offers a promising new technology to support student learning. This article outlines several ways that students can employ these tools to support better learning along with a set of guidelines for all to be wary of when implementing these in an academic setting.

Multi modality fusion transformer with spatio-temporal feature aggregation module for psychiatric disorder diagnosis

Bipolar disorder (BD) is characterized by recurrent episodes of depression and mild mania. In this paper, to address the common issue of insufficient accuracy in existing methods and meet the requirements of clinical diagnosis, we propose a framework called Spatio-temporal Feature Fusion Transformer (STF2Former). It improves on our previous work - MFFormer by introducing a Spatio-temporal Feature Aggregation Module (STFAM) to learn the temporal and spatial features of rs-fMRI data. It promotes intra-modality attention and information fusion across different modalities. Specifically, this method decouples the temporal and spatial dimensions and designs two feature extraction modules for extracting temporal and spatial information separately. Extensive experiments demonstrate the effectiveness of our proposed STFAM in extracting features from rs-fMRI, and prove that our STF2Former can significantly outperform MFFormer and achieve much better results among other state-of-the-art methods.

Multi-agent Continuous Control with Generative Flow Networks

Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving as a powerful alternative to reinforcement learning for exploratory control tasks. However, the individual-flow matching constraint in GFlowNets limits their applications for multi-agent systems, especially continuous joint-control problems. In this paper, we propose a novel Multi-Agent generative Continuous Flow Networks (MACFN) method to enable multiple agents to perform cooperative exploration for various compositional continuous objects. Technically, MACFN trains decentralized individual-flow-based policies in a centralized global-flow-based matching fashion. During centralized training, MACFN introduces a continuous flow decomposition network to deduce the flow contributions of each agent in the presence of only global rewards. Then agents can deliver actions solely based on their assigned local flow in a decentralized way, forming a joint policy distribution proportional to the rewards. To guarantee the expressiveness of continuous flow decomposition, we theoretically derive a consistency condition on the decomposition network. Experimental results demonstrate that the proposed method yields results superior to the state-of-the-art counterparts and better exploration capability. Our code is available at https://github.com/isluoshuang/MACFN.

BAGAIL: Multi-modal imitation learning from imbalanced demonstrations

Expert demonstrations in imitation learning often contain different behavioral modes, e.g., driving modes such as driving on the left, keeping the lane, and driving on the right in the driving tasks. Although most existing multi-modal imitation learning methods allow learning from demonstrations of multiple modes, they have strict constraints on the data of each mode, generally requiring a near data ratio of all modes. Otherwise, it tends to fall into a mode collapse or only learn the data distribution of the mode that has the largest data volume. To address the problem, an algorithm that balances real-fake loss and classification loss by modifying the output of the discriminator, referred to as BAlanced Generative Adversarial Imitation Learning (BAGAIL), is proposed. With this modification, the generator is only rewarded for generating real trajectories with correct modes. BAGAIL is therefore able to deal with imbalanced expert demonstrations and carry out efficient learning for each mode. The learning process of BAGAIL is divided into a pre-training stage and an imitation learning stage. During the pre-training stage, BAGAIL initializes the generator parameters by means of conditional Behavioral Cloning, laying the foundation for the direction of parameter optimization. During the imitation learning stage, BAGAIL optimizes the parameters by using the adversary between the generator and the modified discriminator so that the finally obtained policy can successfully learn the distribution of imbalanced expert data. The experiments showed that BAGAIL accurately distinguished different behavioral modes with imbalanced demonstrations. What is more, the learning result of each mode is close to the expert standard and more stable than other multi-modal imitation learning methods.

Nurses' self-regulated learning in clinical wards: Important insights for nurse educators from a multi-method research study

Central in nurse education curricula stands the preparation of future nurses to work in quickly evolving, dynamic, clinical wards. Learning in the flow of work plays a pivotal role in initial nurse education, but also during continuous professional development. To drive their ongoing development, nurses need competency in self-regulation of learning (SRL). Despite the importance of SRL in the clinical workplace for all (future) healthcare professionals, research on self-regulated workplace learning (SRwpL) of nurses and future nurses in clinical wards is underdeveloped. This study aims to enhance the conceptual understanding of SRwpL strategies and practices in clinical nursing wards and to offer insights for designing effective educational interventions supporting the facilitation and development of (future) nurses' SRwpL in the clinical ward. A multi-actor, multi-method perspective was adopted to qualitatively investigate SRwpL strategies nurses engaged in. Nurses were observed and interviewed, but also professionals responsible for ongoing development in clinical wards (the ward's head nurses and learning counselors) were interviewed. The data collection took place before the COVID pandemic. Results reveal self-regulatory strategies conditional for SRwpL in addition to strategies initiating, progressing, and evaluating the learning process. Head nurses and learning counselors report a lack of these conditional strategies and little variation, and sporadic engagement in all other self-regulatory strategies. To enhance (future) nurses' SRwpL, we suggest that clinical supervisors from educational institutions could exert a lasting influence by not only educating student nurses, but also fostering further professional development of counselors and head nurses to scaffold the SRwpL processes of future nurses in clinical wards.

Enhancing nursing education for Indigenous students: Indigenous nursing students' insights and strategies

BACKGROUND

Indigenous nursing students contended with far-reaching challenges during the pandemic that significantly altered their experiences of nursing education. These experiences are poorly understood by nursing educators and strategies for Indigenous nursing student success rarely involve the insights of current Indigenous nursing students.

AIM

The aim of this article is to offer Indigenous-student derived recommendations regarding strategies for improving their experiences and success within nursing education during the pandemic and beyond.

DESIGN AND METHODS

This qualitative study employed an Indigenous methodology including land-based learning, ceremony, and sharing circles.

PARTICIPANTS AND SETTING

Indigenous nursing students (n = 17) from a Western Canadian school of nursing participated in one of three sharing circles.

RESULTS

Indigenous nursing students recommended institutional and program adaptations along with increases to cultural safety for enhancing their experience in nursing education. Institutional and program strategies included: decreasing course loads and class sizes; an Indigenous-specific cohort; a transition program after course failure; increasing academic supports such as additional clinical skills and academic writing practice. Recommendations for increasing cultural safety included: mandatory and recurrent cultural safety training for faculty, staff and students; differential learning and evaluation strategies; and increased inclusion of Indigenous ceremonies and practices.

CONCLUSION

The findings of this study provide insights that can help guide curriculum development, pedagogical approaches, and policy development to improve nursing education for Indigenous students.

An elastic competitive and discriminative collaborative representation method for image classification

Collaborative representation-based (CR) methods have become prevalent for pattern classification tasks, achieving formidable performance. Theoretically, we expect the learned class-specific representation of the correct class to be discriminative against others, with the representation of the correct class contributing dominantly in CR. However, most existing CR methods focus on improving discrimination while having a limited impact on enhancing the representation contribution of the correct category. In this work, we propose a novel CR approach for image classification called the elastic competitive and discriminative collaborative representation-based classifier (ECDCRC) to simultaneously strengthen representation contribution and discrimination of the correct class. The ECDCRC objective function penalizes two key terms by fully incorporating label information. The competitive term integrates the nearest subspace representation with corresponding elastic factors into the model, allowing each class to have varying competition intensities based on similarity with the query sample. This enhances the representation contribution of the correct class in CR. To further improve discrimination, the discriminative term introduces an elastic factor as a weight in the model to represent the gap between the query sample and the representation of each class. Moreover, instead of focusing on representation coefficients, the designed ECDCRC weights associated with representation components directly relate to the representation of each class, enabling more direct and precise discrimination improvement. Concurrently, sparsity is also enhanced through the two terms, further boosting model performance. Additionally, we propose a robust ECDCRC (R-ECDCRC) to handle image classification with noise. Extensive experiments on seven public databases demonstrate the proposed method's superior performance over related state-of-the-art CR methods.

Active recall strategies associated with academic achievement in young adults: A systematic review

BACKGROUND

Effective learning strategies are crucial to the development of academic skills and information retention, especially in post secondary education where increasingly complex subjects are explored. Active recall-based strategies have been identified as particularly effective for long-term learning. This systematic review investigates the effectiveness of various active recall-based learning strategies for improving academic performance and self-efficacy in higher education students.

METHODS

A systematic review of peer-reviewed articles was conducted with a priori criteria by searching PubMed, ScienceDirect, JSTOR, PsycInfo, and Web of Science databases. Search results were screened/extracted and reconciled by two independent authors with the use of a piloted screening tool. Included studies were assessed for quality and risk of bias using the GRADE Quality Assessment Tool for Quantitative Studies. Three overarching study strategies were extracted for further investigation including flashcards, practice testing or retrieval practice, and concept mapping. Within each category, three additional unique search strings were searched, screened, and extracted. A qualitative analysis of the studies was provided.

RESULTS

Among the appraised articles, flashcards were found to be popular and correlated with higher GPA and test scores. Self-testing, retrieval practice, and concept mapping were also effective but under-utilized. Concept mapping was found to boost student confidence.

CONCLUSION

Active recall strategies exhibit promise for effective learning and additional research in these developing field can support academic pursuits.

Overcoming the Barriers to Teaching Teamwork to Undergraduates in STEM

There is widespread recognition that undergraduate students in the life sciences must learn how to work in teams. However, instructors who wish to incorporate teamwork into their classrooms rarely have formal training in how to teach teamwork. This is further complicated by the application of synonymous and often ambiguous terminology regarding teamwork that is found in literature spread among many different disciplines. There are significant barriers for instructors wishing to identify and implement best practices. We synthesize key concepts in teamwork by considering the knowledge, skills, and attitudes (KSAs) necessary for success, the pedagogies and curricula for teaching those KSAs, and the instruments available for evaluating and assessing success. There are only a limited number of studies on teamwork in higher education that present an intervention with a control group and a formal evaluation or assessment. Moreover, these studies are almost exclusively outside STEM disciplines, raising questions about their extensibility. We conclude by considering how to build an evidence base for instruction that will empower students with the KSAs necessary for participating in a lifetime of equitable and inclusive teamwork.

PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder

Anomaly detection is an important yet challenging task in medical image analysis. Most anomaly detection methods are based on reconstruction, but the performance of reconstruction-based methods is limited due to over-reliance on pixel-level losses. To address the limitation, we propose a patch-wise contrastive learning-based auto-encoder for medical anomaly detection. The key contribution is the patch-wise contrastive learning loss that provides supervision on local semantics to enforce semantic consistency between corresponding input-output patches. Contrastive learning pulls corresponding patch pairs closer while pushing non-corresponding ones apart between input and output, enabling the model to learn local normal features better and improve discriminability on anomalous regions. Additionally, we design an anomaly score based on local semantic discrepancies to pinpoint abnormalities by comparing feature difference rather than pixel variations. Extensive experiments on three public datasets (i.e., brain MRI, retinal OCT, and chest X-ray) achieve state-of-the-art performance, with our method achieving over 99% AUC on retinal and brain images. Both the contrastive patch-wise supervision and patch-discrepancy score provide targeted advancements to overcome the weaknesses in existing approaches.

Older adults' name-face association learning is facilitated for names with high-frequency first syllables

Older adults have even greater difficulty learning name-face associations than young adults, although many variables reflecting properties of the names have been shown to affect young and older adults' name learning similarly. Older adults' name-face association learning was compared for names with high-frequency (HF) first syllables versus names with low-frequency (LF) first syllables. Twenty-eight adults ages 65 to 80 learned five names with HF first syllables and five names with LF first syllables in association with 10 new faces over repeated testing rounds with feedback. Participants learned more name-face associations when the names had HF first syllables than LF first syllables. Findings indicate that older adults benefit from increased frequency of phonological segments within a word on a task other than word retrieval and are consistent with a theoretical framework that accounts for learning new name-face associations, the effects of linguistic properties of the names, and ageing.

Goal-oriented inference of environment from redundant observations

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related "core states" from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed.

SecureNet: Proactive intellectual property protection and model security defense for DNNs based on backdoor learning

With the widespread application of deep neural networks (DNNs), the risk of privacy breaches against DNN models is constantly on the rise, resulting in an increasing need for intellectual property (IP) protection for such models. Although neural network watermarking techniques are widely used to safeguard the IP of DNNs, they can only achieve passive protection and cannot actively prevent unauthorized users from illicit use or embezzlement of the trained DNN models. Therefore, the development of proactive protection techniques to prevent IP infringement is imperative. To this end, we propose SecureNet, a key-based access license framework for DNN models. The proposed approach involves injecting license keys into the model through backdoor learning, enabling correct model functionality only when the appropriate license key is included in the input. To ensure the reusability of DNN models, we also propose a license key replacement algorithm. In addition, based on SecureNet, we designed defense mechanisms against adversarial attacks and backdoor attacks, respectively. Furthermore, we introduce a fine-grained authorization method that enables flexible granting of model permissions to different users. We have designed four license-key schemes with different privileges, tailored to various scenarios. We evaluated SecureNet on five benchmark datasets including MNIST, Cifar10, Cifar100, FaceScrub, and CelebA, and assessed its performance on six classic DNN models: LeNet-5, VGG16, ResNet18, ResNet101, NFNet-F5, and MobileNetV3. The results demonstrate that our approach outperforms the state-of-the-art model parameter encryption methods by at least 95% in terms of computational efficiency. Additionally, it provides effective defense against adversarial attacks and backdoor attacks without compromising the model's overall performance.

Attention-based investigation and solution to the trade-off issue of adversarial training

Adversarial training has become the mainstream method to boost adversarial robustness of deep models. However, it often suffers from the trade-off dilemma, where the use of adversarial examples hurts the standard generalization of models on natural data. To study this phenomenon, we investigate it from the perspective of spatial attention. In brief, standard training typically encourages a model to conduct a comprehensive check to input space. But adversarial training often causes a model to overly concentrate on sparse spatial regions. This reduced tendency is beneficial to avoid adversarial accumulation but easily makes the model ignore abundant discriminative information, thereby resulting in weak generalization. To address this issue, this paper introduces an Attention-Enhanced Learning Framework (AELF) for robustness training. The main idea is to enable the model to inherit the attention pattern of standard pre-trained model through an embedding-level regularization. To be specific, given a teacher model built on natural examples, the embedding distribution of teacher model is used as a static constraint to regulate the embedding outputs of the objective model. This design is mainly supported with that the embedding feature of standard model is usually recognized as a rich semantic integration of input. For implementation, we present a simplified AELFs that can achieve the regularization with single cross entropy loss via the parameter initialization and parameter update strategy. This avoids the extra consistency comparison operation between embedding vectors. Experimental observations verify the rationality of our argument, and experimental results demonstrate that it can achieve remarkable improvements in generalization under the high-level robustness.

Communication-efficient distributed cubic Newton with compressed lazy Hessian

Recently, second-order distributed optimization algorithms have been becoming a research hot in distributed learning, due to their faster convergence rate than the first-order algorithms. However, second-order algorithms always suffer from serious communication bottleneck. To conquer such challenge, we propose communication-efficient second-order distributed optimization algorithms in the parameter-server framework, by incorporating cubic Newton methods with compressed lazy Hessian. Specifically, our algorithms require each worker communicate compressed Hessians with the server only at some particular iterations, which can save both communication bits and communication rounds. For non-convex problems, we theoretically prove that our algorithms can reduce the communication cost comparing to the state-of-the-art second-order algorithms, while maintaining the same iteration complexity order O(ϵ-3/2) as the centralized cubic Newton methods. By further using gradient regularization technique, our algorithms can achieve global convergence for convex problems. Moreover, for strongly convex problems, our algorithms can achieve local superlinear convergence rate without any requirement on initial conditions. Finally, numerical experiments are conducted to show the high efficiency of the proposed algorithms.

Variations in Student Approaches to Problem Solving in Undergraduate Biology Education

Existing research has investigated student problem-solving strategies across science, technology, engineering, and mathematics; however, there is limited work in undergraduate biology education on how various aspects that influence learning combine to generate holistic approaches to problem solving. Through the lens of situated cognition, we consider problem solving as a learning phenomenon that involves the interactions between internal cognition of the learner and the external learning environment. Using phenomenography as a methodology, we investigated undergraduate student approaches to problem solving in biology through interviews. We identified five aspects of problem solving (including knowledge, strategy, intention, metacognition, and mindset) that define three qualitatively different approaches to problem solving; each approach is distinguishable by variations across the aspects. Variations in the knowledge and strategy aspects largely aligned with previous work on how the use or avoidance of biological knowledge informed both concept-based and nonconcept-based strategies. Variations in the other aspects revealed intentions spanning complete disengagement to deep interest with the course material, different degrees of metacognitive reflections, and a continuum of fixed to growth mindsets. We discuss implications for how these characterizations can improve instruction and efforts to support development of problem-solving skills.

From whole numbers to fractions to word problems: Hierarchical relations in mathematics knowledge for Chinese Grade 6 students

It is well established in the literature that fraction knowledge is important for learning more advanced mathematics, but the hierarchical relations among whole number arithmetic, fraction knowledge, and mathematics word problem-solving are not well understood. In the current study, Chinese Grade 6 students (N = 1160; 465 girls; Mage = 12.1 years, SD = 0.6) completed whole number arithmetic (addition, subtraction, multiplication, and division), fraction (mapping, equivalence, comparison, and arithmetic), and mathematics word problem-solving assessments. They also completed two control measures: number writing speed and nonverbal intelligence. Structural equation modeling was used to investigate the hierarchical relations among these assessments. Among the four fraction tasks, the correlations were low to moderate, suggesting that each task may tap into a unique aspect of fraction understanding. In the model, whole number arithmetic was directly related to all four fraction tasks, but was only indirectly related to mathematics word problem-solving, through fraction arithmetic. Only fraction arithmetic, the most advanced fraction skill, directly predicted mathematics word problem-solving. These findings are consistent with the view that students need to build these associations into their mathematics hierarchy to advance their mathematical competence.

Remediation in clinical practice for student nurses - A scoping review

BACKGROUND

Clinical practice provides an opportunity for undergraduate nursing students to develop the professional attitudes, knowledge and skills required for the delivery of safe competent care. Some students, however, are challenged to consolidate learning in clinical practice and are therefore at risk of failing courses or programmes. Supporting these students requires remediation strategies specific to clinical practice. This is challenging, however, as remediation approaches frequently centre on supporting students in theoretical components of courses/programmes, rather than clinical practice.

OBJECTIVES

A scoping review was conducted to explore and summarise literature in undergraduate nurse education related to remediation support for clinical practice.

METHODS

The research question was developed using the Population/Concept/Context model. Following the identification of keywords, five databases (CINHAL Plus, MEDLINE, ProQuest, Scopus and Informit) were searched. Abstracts and full-text articles were independently screened by two authors. Data from included studies was extracted and then thematically analysed.

RESULTS

Twenty papers met inclusion/exclusion criteria (five literature reviews, one concept analysis, six commentary papers and eight original research studies). Research studies used qualitative, quantitative, or mixed methods research designs. Populations included nurse academics, mentors/preceptors, health professional students (including nursing students) and nursing students exclusively. Three themes were identified: pre-placement remediation strategies; on-placement remediation strategies and post-placement remediation strategies. All authors highlighted the importance of remediation for at-risk students in clinical practice.

CONCLUSIONS

This review identified several remediation strategies that may support undergraduate nursing students in clinical practice. Few, however, were well-defined or rigorously evaluated, highlighting the need for additional research on nursing student remediation in clinical practice. A partnership-based approach to remediation that engages students, educators, and healthcare providers and is underpinned by clear processes may be of further benefit to nursing students in clinical practice.

Game-based learning enhances students' understanding of endocrine physiology in veterinary medicine

Endocrine physiology is a complex subject for students. Game-based learning (GBL) and case-based learning (CBL) are active methodologies that are widely used because of their potential for motivation and greater proximity to the reality of modern students. We evaluated the effectiveness of GBL and CBL among veterinary medicine students compared with a control group using peer tutoring. Students (n = 106) from two institutions volunteered to participate in this study. The participants were submitted to a pretest questionnaire and subsequently were divided into three paired groups by their performance on the pretest exam: 1) traditional class + peer tutoring, 2) traditional class + GBL, and 3) traditional class + CBL. After the students completed the activities, their performance was once again evaluated by applying a new test with the same initial 10 questions and another set of 10 different questions. The students' perceptions and satisfaction with the methodologies and learning strategies were assessed. Anxiety was assessed with the State-Trait Anxiety Inventory before and after the conventional class and after the active methodologies. The GBL group significantly improved their correct answers compared with the baseline (P < 0.05), with no significant difference from CBL and peer tutoring. Anxiety levels did not differ regardless of the time of evaluation or the teaching methodology applied. GBL promoted a greater perception of the stimulus for self-study and problem-solving ability and contributed to the development of group dynamics compared with the group who received CBL (P < 0.05). In conclusion, GBL showed better results than peer tutoring and CBL.NEW & NOTEWORTHY We compared the supplementary use of game-based learning, case-based learning, and peer tutoring in the study of endocrine physiology by veterinary students and observed a slight advantage for game-based learning over the other two methodologies. The game was developed by the authors and is an unprecedented tool that can prove useful to improve knowledge acquisition in students of veterinary medicine. Thus, game-based learning is an effective supplementary teaching strategy.

Teaching critical thinking in nutritional sciences: a model course and assignments

Critical thinking is a common and important learning outcome in college curricula. Case-based and problem-based learning can be used to assess and foster critical thinking skills. HNF 250-Contemporary Issues in Human Nutrition is a critical thinking course developed during the redesign of a nutritional sciences major program. Course assignments were designed to assess the course and nutritional sciences major learning outcomes. The nutrition and health claim assignment is scaffolded across the academic semester as three assignments: 1) bibliography assignment; 2) poster presentation; and 3) paper. Course lectures and materials have been designed to prepare students for completion of each assignment. The assignments have been modified over time based on classroom observations and student performance. In 2021, the course learning outcomes were examined by assessing several assignments including the nutrition and health claim poster and paper. Course learning outcome benchmarks using these assessments generally included 80% of students achieving an 80% for each criterion. Results revealed that students were not meeting most of these assessment benchmarks during the 2021 iteration, although benchmark data from other course assessments were more satisfactory. It is possible that the transition from a virtual to an in-person format negatively influenced student performance on these course learning outcomes. This course and the nutrition and health claim assignment example can provide a course design and learning outcome assessment framework for other higher education critical thinking courses.NEW & NOTEWORTHY This paper describes how a scaffolded nutrition and health claim assignment is used to teach critical thinking skills among nutritional sciences students and examine the program's learning outcomes. Further, this course example is to serve as an model for STEM majors on how to incorporate case-based and problem-based learning strategies into an undergraduate course.

Pain neuroscience education for patients with chronic pain: A scoping review from teaching-learning strategies, educational level, and cultural perspective

OBJECTIVE

(1) To identify the characteristics of PNE programs in terms of teaching-learning strategies, session modality, content delivery format, number of sessions, total minutes and instructional support material used in patients with chronic musculoskeletal pain, (2) to describe PNE adaptations for patients with different educational levels or cultural backgrounds, and (3) to describe the influence of the patient's educational level or cultural background on the effects of PNE.

METHODS

The PRISMA guideline for scoping reviews was followed. Nine databases were systematically searched up to July 8, 2023. Articles that examined clinical or psychosocial variables in adults with chronic musculoskeletal pain who received PNE were included.

RESULTS

Seventy-one articles were included. Studies found benefits of PNE through passive/active teaching-learning strategies with group/individual sessions. However, PNE programs presented great heterogeneity and adaptations to PNE were poorly reported. Most studies did not consider educational level and culture in the effects of PNE.

CONCLUSIONS

Despite the large number of studies on PNE and increased interest in this intervention, the educational level and culture are poorly reported in the studies.

PRACTICAL IMPLICATIONS

It is recommended to use passive and/or active teaching-learning strategies provided in individual and/or group formats considering the patient's educational level and culture.