Pandemic response policies' democratizing effects on online learning.

The COVID-19 pandemic has changed peoples' lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and schooling. Can this uptake in online learning help close longstanding skills gaps in the US workforce in a sustainable and equitable manner? We answer this question by analyzing individual engagement data of DataCamp users between October 2019 and September 2020 (n = 277,425). Exploiting the staggered adoption of actions to mitigate the spread of COVID-19 across states, we identify the causal effect at the neighborhood level. The adoption of nonessential business closures led to a 38% increase in new users and a 6% increase in engagement among existing users. We find that these increases are proportional across higher- and lower-income neighborhoods and neighborhoods with a high or low share of Black residents. This demonstrates the potential for online platforms to democratize access to knowledge and skills that are in high demand, which supports job security and facilitates social mobility.

Providing insights into health data science education through artificial intelligence.

BACKGROUND: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students' learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. METHODS: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students' engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. RESULTS: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. CONCLUSIONS: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.

An integrated, modular approach to data science education in microbiology.

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

Hands-on training about overfitting.

Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.

A global perspective on evolving bioinformatics and data science training needs.

Bioinformatics is now intrinsic to life science research, but the past decade has witnessed a continuing deficiency in this essential expertise. Basic data stewardship is still taught relatively rarely in life science education programmes, creating a chasm between theory and practice, and fuelling demand for bioinformatics training across all educational levels and career roles. Concerned by this, surveys have been conducted in recent years to monitor bioinformatics and computational training needs worldwide. This article briefly reviews the principal findings of a number of these studies. We see that there is still a strong appetite for short courses to improve expertise and confidence in data analysis and interpretation; strikingly, however, the most urgent appeal is for bioinformatics to be woven into the fabric of life science degree programmes. Satisfying the relentless training needs of current and future generations of life scientists will require a concerted response from stakeholders across the globe, who need to deliver sustainable solutions capable of both transforming education curricula and cultivating a new cadre of trainer scientists.

Ad hoc efforts for advancing data science education.

With increasing demand for training in data science, extracurricular or "ad hoc" education efforts have emerged to help individuals acquire relevant skills and expertise. Although extracurricular efforts already exist for many computationally intensive disciplines, their support of data science education has significantly helped in coping with the speed of innovation in data science practice and formal curricula. While the proliferation of ad hoc efforts is an indication of their popularity, less has been documented about the needs that they are designed to meet, the limitations that they face, and practical suggestions for holding successful efforts. To holistically understand the role of different ad hoc formats for data science, we surveyed organizers of ad hoc data science education efforts to understand how organizers perceived the events to have gone-including areas of strength and areas requiring growth. We also gathered recommendations from these past events for future organizers. Our results suggest that the perceived benefits of ad hoc efforts go beyond developing technical skills and may provide continued benefit in conjunction with formal curricula, which warrants further investigation. As increasing numbers of researchers from computational fields with a history of complex data become involved with ad hoc efforts to share their skills, the lessons learned that we extract from the surveys will provide concrete suggestions for the practitioner-leaders interested in creating, improving, and sustaining future efforts.

Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy.

Rapid research progress in science and technology (S&T) and continuously shifting workforce needs exert pressure on each other and on the educational and training systems that link them. Higher education institutions aim to equip new generations of students with skills and expertise relevant to workforce participation for decades to come, but their offerings sometimes misalign with commercial needs and new techniques forged at the frontiers of research. Here, we analyze and visualize the dynamic skill (mis-)alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE). The visualizations and computational models presented here can help key decision makers understand the evolving structure of skills so that they can craft educational programs that serve workforce needs. Our study uses millions of publications, course syllabi, and job advertisements published between 2010 and 2016. We show how courses mediate between research and jobs. We also discover responsiveness in the academic, educational, and industrial system in how skill demands from industry are as likely to drive skill attention in research as the converse. Finally, we reveal the increasing importance of uniquely human skills, such as communication, negotiation, and persuasion. These skills are currently underexamined in research and undersupplied through education for the labor market. In an increasingly data-driven economy, the demand for "soft" social skills, like teamwork and communication, increase with greater demand for "hard" technical skills and tools.

Introductory College Mathematics for the Life Sciences: Has Anything Changed?

This paper focuses on issues concerning the introductory college mathematics sequence with an emphasis on students interested in the life sciences, and concentration on the time after the publication of BIO2010 (BIO2010 in Transforming Undergraduate Education for Future Research Biologists, National Academies of Science, Medicine and Engineering, Washington, 2003). It also explores the potential uses of books targeted at introductory mathematics courses for life science majors today. As relevant background, we look at the evolution of the way that calculus has been taught over the past 50 years, including at the high school level. We also explore the implications of changes in technology and course delivery, such as online education. As we discuss different books and introductory course ideas, we focus on the needs of biology students, the inclusion of real-world problems and models, the role of technology, and the impact of data science. The paper is organized as follows: Sect. 1 provides some personal background with calculus dating back to the 1970s, and changes in calculus prior to BIO2010. Section 2 introduces goals for an introductory mathematics sequence and evaluates the calculus sequence in light of those goals. Sections 3-7 discuss various issues that will help to understand issues and challenges for introductory mathematics for the life sciences: Calculus in high school (Sect. 3), equity issues relative to calculus and other math topics (Sect. 4), the impact of online education (Sect. 5), math as a stumbling block for college students (Sect. 6), and the increasing importance and value of teaching data science (Sect. 7). Section 8 reviews the development of books in light of these issues and challenges. The last section (Sect. 9) summarizes conclusions.

Training for translation between disciplines: a philosophy for life and data sciences curricula.

Motivation: Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other. Results: Here we present our experiences with shaping and running a masters' programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula. Supplementary information: Supplementary data are available at Bioinformatics online.

A model to evaluate data science in nursing doctoral curricula.

BACKGROUND: Building on the efforts of the American Association of Colleges of Nursing, we developed a model to infuse data science constructs into doctor of philosophy (PhD) curriculum. Using this model, developing nurse scientists can learn data science and be at the forefront of data driven healthcare. PURPOSE: Here we present the Data Science Curriculum Organizing Model (DSCOM) to guide comprehensive doctoral education about data science. METHODS: Our team transformed the terminology and applicability of multidisciplinary data science models into the DSCOM. FINDINGS: The DSCOM represents concepts and constructs, and their relationships, which are essential to a comprehensive understanding of data science. Application of the DSCOM identified areas for threading as well as gaps that require content in core coursework. DISCUSSION: The DSCOM is an effective tool to guide curriculum development and evaluation towards the preparation of nurse scientists with knowledge of data science.

Identifying Learning Preferences and Strategies in Health Data Science Courses: Systematic Review.

BACKGROUND: Learning and teaching interdisciplinary health data science (HDS) is highly challenging, and despite the growing interest in HDS education, little is known about the learning experiences and preferences of HDS students. OBJECTIVE: We conducted a systematic review to identify learning preferences and strategies in the HDS discipline. METHODS: We searched 10 bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, Wiley Online Library, ScienceDirect, SpringerLink, EBSCOhost, ERIC, and IEEE Xplore) from the date of inception until June 2023. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included primary studies written in English that investigated the learning preferences or strategies of students in HDS-related disciplines, such as bioinformatics, at any academic level. Risk of bias was independently assessed by 2 screeners using the Mixed Methods Appraisal Tool, and we used narrative data synthesis to present the study results. RESULTS: After abstract screening and full-text reviewing of the 849 papers retrieved from the databases, 8 (0.9%) studies, published between 2009 and 2021, were selected for narrative synthesis. The majority of these papers (7/8, 88%) investigated learning preferences, while only 1 (12%) paper studied learning strategies in HDS courses. The systematic review revealed that most HDS learners prefer visual presentations as their primary learning input. In terms of learning process and organization, they mostly tend to follow logical, linear, and sequential steps. Moreover, they focus more on abstract information, rather than detailed and concrete information. Regarding collaboration, HDS students sometimes prefer teamwork, and sometimes they prefer to work alone. CONCLUSIONS: The studies' quality, assessed using the Mixed Methods Appraisal Tool, ranged between 73% and 100%, indicating excellent quality overall. However, the number of studies in this area is small, and the results of all studies are based on self-reported data. Therefore, more research needs to be conducted to provide insight into HDS education. We provide some suggestions, such as using learning analytics and educational data mining methods, for conducting future research to address gaps in the literature. We also discuss implications for HDS educators, and we make recommendations for HDS course design; for example, we recommend including visual materials, such as diagrams and videos, and offering step-by-step instructions for students.

Design, implementation and evaluation of a spiral module combining data science, digital health and evidence-based medicine in the undergraduate medical curriculum: A mixed methods study.

BACKGROUND: Digital health, data science and health informatics are increasingly important in health and healthcare, but largely ignored in undergraduate medical training. METHODS: In a large UK medical school, with staff and students, we co-designed a new, 'spiral' module (with iterative revisiting of content), covering data science, digital health and evidence-based medicine, implementing in September 2019 in all year groups with continuous evaluation and improvement until 2022. RESULTS: In 2018/19, a new module, 'Doctor as Data Scientist', was co-designed by academic staff (n = 14), students (n = 23), and doctors (n = 7). The module involves 22 staff, 120 h (43 sessions: 22 lectures, 15 group and six other) over a 5-year curriculum. Since September 2019, 5,200 students have been taught with good attendance. Module student satisfaction ratings were 92%, 84%, 84% and 81% in 2019, 2020, 2021 and 2022 respectively, compared to the overall course (81%). CONCLUSIONS: We designed, implemented and evaluated a new undergraduate medical curriculum that combined data science and digital health with high student satisfaction ratings.

Undergraduate Degree in Medical Data Science: A Conceptual Framework.

The undergraduate degree program in medical data science aims to train future data scientists with a medical lens to tackle healthcare challenges using a data-driven approach. The program is a collaborative effort within the Berlin University Alliance, addressing the lack of healthcare-focused data science education in Berlin and Germany. The curriculum covers mathematics, informatics, medical informatics, and medicine, featuring diverse didactic formats. Graduates will be equipped to lead data science and digital transformation projects in healthcare.

Assessing Health Data Science Internships: A Comprehensive Study at the University of Lille.

Data Science emerged as a new cross-disciplinary discipline at the intersection of statistics, computer science, and expertise in a specific domain, such as health and biology. The data science field, alongside other data-related professions, is continuously evolving. We conducted a study examining tasks assigned to first-year internship students pursuing a Master's degree in Health Data Science, exploring the missions, technologies employed and skills required, and internship alignment with students' training through semi-structured interviews with 32 participants. Three quarters of the students were placed in teams within the public sector. Among these entities, there were 11 hospitals and 12 universities. Although the majority of students did their internship as part of a methodological team, they often had a healthcare professional on their team. Nearly half of the missions involved descriptive analysis, followed by 9 missions focused on etiology or prediction and 8 missions on implementing a data warehouse. The majority of students had to perform data management and produce graphs, while only half conducted statistical analysis. The findings highlighted that data management remains a major challenge, and it should be taken into consideration when designing training programs. In future, it remains to determine whether this trend will continue with second-year students or if, with experience, they are more often assigned statistical analyses.

Advancing Data Science Competencies for Nursing PhD Students.

Given the evolving importance of data science approaches in nursing research, we developed a 3-credit, 15-week course that is integrated into the second year PhD curriculum at Columbia University School of Nursing. As a complement to didactic content, the students address a research question of their choice using a big data source, Jupyter Notebook, and R programming language. The course evolved over time with generative AI tools being added in 2023. Student self-evaluations of their data science competencies improved from baseline. This case study adds to the evolving body of literature on data science and AI competences in nursing.

A modular inquiry-based semester theme that integrates data science education and bioinformatics in protein structure function courses.

BACKGROUND: With an exponential growth in biological data and computing power, familiarity with bioinformatics has become a demanding and popular skill set both in academia and industry. There is a need to increase students' competencies to be able to take on bioinformatic careers, to get them familiarized with scientific professions in data science and the academic training required to pursue them, in a field where demand outweighs the supply. METHODS: Here we implemented a set of bioinformatic activities into a protein structure and function course of a graduate program. Concisely, students were given hands-on opportunities to explore the bioinformatics-based analyses of biomolecular data and structural biology via a semester-long case study structured as inquiry-based bioinformatics exercises. Towards the end of the term, the students also designed and presented an assignment project that allowed them to document the unknown protein that they identified using bioinformatic knowledge during the term. RESULTS: The post-module survey responses and students' performances in the lab module imply that it furthered an in-depth knowledge of bioinformatics. Despite having not much prior knowledge of bioinformatics prior to taking this module students indicated positive feedback. CONCLUSION: The students got familiar with cross-indexed databases that interlink important data about proteins, enzymes as well as genes. The essential skillsets honed by this research-based bioinformatic pedagogical approach will empower students to be able to leverage this knowledge for their future endeavours in the bioinformatics field.