Using learning analytics to evaluate a video-based lecture series

Podcast-Based Medical Education and Content Alignment with National Education Bodies

Introduction

Despite the rapid expansion of medical education podcasts in the past decade, there are few efforts to characterize the landscape of available content for specific medical specialties. We trialed a method of rigorous characterization for the field of neurology.

Materials and Methods

Using a censoring date of July 25, 2022, we queried the top three podcast platforms for neurology education podcasts: Apple Podcasts, Spotify, and Google Podcasts. We characterized podcasts based on total number of episodes, episode release frequency, target audience, and affiliation type. We characterized individual episodes by length and primary content area as defined by the Accreditation Council for Graduate Medical Education (ACGME) and American Board of Psychiatry and Neurology (ABPN). We compared content availability with content recommendations from these education bodies.

Results

We identified 30 podcasts sharing 1772 episodes and totaling 46,287 min. The ACGME subspecialties most frequently covered were vascular neurology (5082 total min, 11%), neuroimmunology (4,406, 10%), and neuromuscular diseases (3,771, 8%). Subjects that were underrepresented included palliative neurology (89 min, 0.2%), neuropathology (95 min, 0.2%), and bioethics (171 min, 0.4%). The coverage of ABPN examination topics varied substantially from the content distribution for the examination.

Discussion

The current landscape of neurology education podcasts features heterogeneous coverage of topics and varies considerably from recommended distribution of content by national education bodies. As podcasts have tremendous potential in supplementing neurology education, characterizing available content may help various stakeholders in the neurology education pipeline optimize the use of this e-learning modality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40670-023-01971-0.

What Do You See? Signature Pedagogy in Continuous Electroencephalography Teaching

PURPOSE

Electroencephalography (EEG) is commonly used in neurology, but there is variability in how neurologists interpret EEGs, potentially from variability in EEG teaching. Little is known about how EEG teaching is done to prepare neurologists for professional practice.

METHODS

We interviewed a group of EEG experts to characterize their teaching practices around continuous EEG (cEEG). We used signature pedagogy as a framework to analyze and interpret the data.

RESULTS

We identified pervasive and characteristic forms of cEEG teaching. Teaching is based on apprenticeship, relying on "learning by doing" in the context of real-life clinical practice. There are habitual steps that learners take to anchor teaching, which typically occurs during rounds. There is a common language and core knowledge that trainees need to master early in their training.

CONCLUSIONS

There are pervasive characteristic forms of cEEG teaching. These findings can help facilitate instructional design and implementation of complementary or enhanced cEEG teaching practices.

Evaluating video-based lectures on YouTube for dental education

PURPOSE/OBJECTIVES

Multimedia presentations and online platforms are used in dental education. Though studies indicate the benefits of video-based lectures (VBLs), data regarding user reception and optimal video features in dental education are limited, particularly on Web 2.0 platforms like YouTube. Given increasing technology integration and remote learning, dental educators need evidence to guide implementation of YouTube videos as a freely available resource. The purpose of this study is to determine video metrics, viewership and format efficacy for dental education videos.

METHODS

First, a cross-sectional survey was conducted of viewers (N = 683) of the Mental Dental educational videos on YouTube. Analytics were evaluated for 677 200 viewers to assess audience demographics, retention and optimal video length. Second, a randomized crossover study was conducted of dental students (N = 101) who watched VBLs in either slideshow or pencast formats and were tested on content learning to compare format efficacy.

RESULTS

Most viewers of Mental Dental videos were dental students (44.2%) and professionals (37.8%) who would likely recommend the platform to a friend or colleague (Net Promoter Score = 82.1). Audience retention declined steadily at 1.34% per minute, independent of video length. Quiz performance did not differ between slideshow and pencast videos, with students having a slight preference for slideshows (P = 0.049).

CONCLUSIONS

Dental students and professionals use VBLs and are likely to recommend them to friends and colleagues. There is no optimal video length to maximize audience retention and lecture format (slideshow vs. pencast) does not significantly impact content learning. Results can guide implementation of VBLs in dental curricula.

Role of convolutional features and machine learning for predicting student academic performance from MOODLE data

Predicting student performance automatically is of utmost importance, due to the substantial volume of data within educational databases. Educational data mining (EDM) devises techniques to uncover insights from data originating in educational settings. Artificial intelligence (AI) can mine educational data to predict student performance and provide measures to help students avoid failing and learn better. Learning platforms complement traditional learning settings by analyzing student performance, which can help reduce the chance of student failure. Existing methods for student performance prediction in educational data mining faced challenges such as limited accuracy, imbalanced data, and difficulties in feature engineering. These issues hindered effective adaptability and generalization across diverse educational contexts. This study proposes a machine learning-based system with deep convoluted features for the prediction of students' academic performance. The proposed framework is employed to predict student academic performance using balanced as well as, imbalanced datasets using the synthetic minority oversampling technique (SMOTE). In addition, the performance is also evaluated using the original and deep convoluted features. Experimental results indicate that the use of deep convoluted features provides improved prediction accuracy compared to original features. Results obtained using the extra tree classifier with convoluted features show the highest classification accuracy of 99.9%. In comparison with the state-of-the-art approaches, the proposed approach achieved higher performance. This research introduces a powerful AI-driven system for student performance prediction, offering substantial advancements in accuracy compared to existing approaches.

Integrating Virtual Teaching in a New Era of Medical Education: Lessons from a Neurology Course

The COVID-19 pandemic has led to a sudden shift toward virtual learning in neurology education, which presents challenges for educators. However, virtual learning is here to stay for three key reasons: demand among students, ease of dissemination, and potential to improve educational quality. Despite challenges, educators can teach effectively using appropriate virtual tools and methods, with innovative approaches that will ultimately lead to sustained improvements in neurology education. Here, we aim to help educators effectively incorporate virtual instruction into their "new normal" by offering practical, evidence-based tips for balancing in-person and virtual learning, selecting the appropriate tools and methods for virtual teaching, and creating a supportive virtual learning environment. Using a systematic approach, educators can identify specific, achievable goals, determine the content's scope, appropriate assessments, select appropriate teaching methods, and create the session schedule and materials. Here we described evidence-based strategies for best practices, developing virtual material, and creating the appropriate virtual learning environment.

Profiling students via clustering in a flipped clinical skills course using learning analytics

Flipped classrooms have become popular as a student-centered approach in medical education because they allow students to improve higher-order thinking skills and problem-solving applications during in-class activities. However, students are expected to study videos and other class materials before class begins. Learning analytics and unsupervised machine learning algorithms (clustering) can be used to examine the pre-class activities of these students to identify inadequate student preparation before the in-class stage and make appropriate interventions. Furthermore, the students' profiles, which provide their interaction strategies towards online materials, can be used to design appropriate interventions. This study investigates student profiles in a flipped classroom. The learning management system interactions of 375 medical students are collected and preprocessed. The k-means clustering algorithms examined in this study show a two-cluster structure: 'high interaction' and 'low-interaction.' These results can be used to help identify low-engaged students and give appropriate feedback.

Understanding the ongoing learning needs of Australian paediatricians: Evaluation of a pilot paediatric video teaching programme

AIM

The purpose of this study was to evaluate whether pre-recorded video-based lectures (VBLs) covering a range of paediatric topics are an acceptable means of providing ongoing education for consultant and trainee paediatricians in Australia.

METHODS

Previous participants (paediatric consultants and junior medical officers) of a neurology outreach teleconference programme offered by a paediatric neurologist between 2017 and 2020 were invited to participate in a multi-specialty pre-recorded video-based education programme. Acceptability was explored by assessing relevance, likelihood of utilising VBL's in the future, uptake and learning activity preferences. The impact of VBLs on confidence, currency and practice was also explored. Additional data including topics of interest, preferred video format, duration, viewing method and frequency of delivery were captured, to better understand participant preferences to inform future efforts.

RESULTS

A total of 135 consented; 116 returned baseline; 94 returned follow-up surveys. Preferred learning activities included a live/interactive component. Videos were considered relevant. Preferences for pre-recorded videos improved from ninth to sixth most preferred learning activity post-intervention. VBL convenience and accessibility were valued. Practice was altered in: approach to management, use of treatments, confidence in decision-making, and discussion with families and patients. The average view duration was 16 min. Longer videos yielded slightly lower audience retention rates. For future offerings, the majority endorsed a preference for a 'mixed' video format and duration of 20-40 min, offered monthly.

CONCLUSION

Video-based medical education is an appealing and sustainable alternative, given the convenience of unrestricted accessibility, in meeting ongoing learning needs of Australian paediatricians and trainees.

The use of video clickstream data to predict university students' test performance: A comprehensive educational data mining approach

Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students' test performance with two consecutive experiments. The first experiment was performed as an exploratory study with 22 university students using a single test performance measure and basic statistical techniques. The second experiment was performed as a conclusive study with 16 students using repeated measures and comprehensive data mining techniques. The findings show that a positive correlation exists between the total number of clicks and students' test performance. Those students who performed a high number of clicks, slow backward speed or doing backwards or pauses achieved better test performance than those who performed a lower number of clicks, or who used fast-backward or fast-forward. In addition, students' test performance could be predicted using video clickstream data with a good level of accuracy (Root Mean Squared Error Percentage (%RMSE) ranged between 15 and 20). Furthermore, the mean of backward speed, number of pauses, and number/percentage of backwards were found to be the most important indicators in predicting students' test performance. These findings may help educators or researchers identify students who are at risk of failure. Finally, the study provides design suggestions based on the findings for the preparation of video-based lectures.

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students’ performance, thus mitigating the probability of student failures. Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students’ performance through school and home tutoring. Students’ educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students’ performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.

Using Technology Adoption Theories to Maximize the Uptake of E-learning in Medical Education

INTRODUCTION

While the use of e-learning tools in medical education is guided by robust literature on their design and evaluation, there is sparse literature on strategies that maximize their adoption among trainees.

METHODS

In this scoping review, we searched Web of Science for studies on technology adoption theories as applied to education, using a final censoring date of August 1, 2021.

RESULTS

Based on our findings, we identified three representative theories: (1) technology acceptance model, (2) technology adoption life cycle, and (3) domestication theory.

DISCUSSION

We describe these theories in detail, examine their prior applications, and propose specific uses within medical e-learning.

Digital Divide in Online Education During the COVID-19 Pandemic: A Cosmetic Course From the View of the Regional Socioeconomic Distribution

Objectives: During the pandemic, quarantine has led to the lockdown of many physical educational institutions. Thus, massive open online courses (MOOCs) have become a more common choice for participants. MOOCs are often flagged as supplemental methods to educational disparities caused by regional socioeconomic distribution. However, dissenters argue that MOOCs can exacerbate the digital divide. This study aimed to compare the participants' performance before and after the outbreak of COVID-19, analyze the impact of the epidemic on online education of cosmetic dermatology from the view of the regional socioeconomic distribution, and investigate whether MOOCs exacerbate the digital divide in the COVID-19 epidemic.

Methods: The study was conducted in participants of the MOOC course Appreciation and Analysis of Cosmetics from January 2018 to December 2020. Based on the platform data and official socioeconomic statistics, correlation of multivariate analysis was used to determine the factors related to the number of total participants. A panel regression model and stepwise least squares regression analysis (STEPLS) were employed to further analyze the relationship between GDP, population, number of college students and number of total participants in different years in the eastern, central and western regions of China.

Results: The number of total participants in 2020 surged 82.02% compared with that in 2019. Completion rates were generally stable in 2018 and 2019 before the COVID-19 pandemic and significantly decreased in 2020 after the outbreak of the pandemic. GDP was the most important socioeconomic factor that determined the total number of participants and it was positively related to the total number of participants before and after the outbreak of the pandemic. The number of college students was unrelated to the total number of participants before the epidemic, and after the outbreak of COVID-19 in 2020, the number became positively related in all regions of China.

Conclusions: This study shows that the epidemic pushes more people to choose MOOCs to study cosmetic dermatology, and online education could exacerbate rather than reduce disparities that are related to regional and socioeconomic status in the cosmetic field in the COVID-19 pandemic.

COVID-19 Restrictions Presented Opportunities and Challenges for Plastic Surgery Residents

BACKGROUND

Restrictions placed during the COVID-19 pandemic to prevent viral spread led to substantial changes in surgical resident education. The aim of this study was to assess the positive and negative impact of COVID-19 on plastic surgery education and training and provide recommendations for continued competency.

METHODS

A cross-sectional online survey of plastic surgery residents across Canada was used to evaluate the impact of COVID-19 on clinical exposure, experience with virtual education, and long-term impact of COVID-19 on surgical training.

RESULTS

This study included 61 plastic surgery residents (40% participation rate). Common educational modalities used during COVID-19 included online seminars (95%) and workshops (58%). Teaching sessions were effective if structured around patient cases (72%), recorded (66%), and limited to 1 hour (64%). There were mixed reactions towards online education sessions; residents reported feeling grateful (54%), motivated (38%), enthusiastic (28%), overwhelmed (41%), pressured to participate (23%), and anxious (13%). There were significantly less residents who felt that their clinical exposure was sufficient during (21%) versus before (72%) pandemic restrictions (P < .001). Overall, 87% of residents felt that the pandemic had a negative impact on their training, surgical skill development, fellowship plans, and job prospects.

CONCLUSIONS

During the initial wave of COVID-19, residents faced altered educational opportunities, which elicited positive and negative emotions with concern regarding surgical skill development and impact on future career plans. Characterizing early educational impact on residency training to identify opportunities for change is worthwhile as the overall effect of the pandemic is ongoing and remains uncertain.

Dataset of Students’ Performance Using Student Information System, Moodle and the Mobile Application “eDify”

The data presented in this article comprise an educational dataset collected from the student information system (SIS), the learning management system (LMS) called Moodle, and video interactions from the mobile application called “eDify.” The dataset, from the higher educational institution (HEI) in Sultanate of Oman, comprises five modules of data from Spring 2017 to Spring 2021. The dataset consists of 326 student records with 40 features in total, including the students’ academic information from SIS (which has 24 features), the students’ activities performed on Moodle within and outside the campus (comprising 10 features), and the students’ video interactions collected from eDify (consisting of six features). The dataset is useful for researchers who want to explore students’ academic performance in online learning environments, and will help them to model their educational datamining models. Moreover, it can serve as an input for predicting students’ academic performance within the module for educational datamining and learning analytics. Furthermore, researchers are highly recommended to refer to the original papers for more details.

Use of Commercially Produced Medical Education Videos in a Cardiovascular Curriculum: Multiple Cohort Study

BACKGROUND

Short instructional videos can make learning more efficient through the application of multimedia principles, and video animations can illustrate the complex concepts and dynamic processes that are common in health sciences education. Commercially produced videos are commonly used by medical students but are rarely integrated into curricula.

OBJECTIVE

Our goal was to examine student engagement with medical education videos incorporated into a preclinical Cardiovascular Systems course.

METHODS

Students who took the first-year 8-week Cardiovascular Systems course in 2019 and 2020 were included in the study. Videos from Osmosis were recommended to be watched before live sessions throughout the course. Video use was monitored through dashboards, and course credit was given for watching videos. All students were emailed electronic surveys after the final exam asking about the course's blended learning experience and use of videos. Osmosis usage data for number of video views, multiple choice questions, and flashcards were extracted from Osmosis dashboards.

RESULTS

Overall, 232/359 (64.6%) students completed surveys, with rates by class of 81/154 (52.6%) for MD Class of 2022, 39/50 (78%) for MD/MPH Class of 2022, and 112/155 (72.3%) for MD Class of 2023. Osmosis dashboard data were available for all 359 students. All students received the full credit offered for Osmosis engagement, and learning analytics demonstrated regular usage of videos and other digital platform features. Survey responses indicated that most students found Osmosis videos to be helpful for learning (204/232, 87.9%; P=.001) and preferred Osmosis videos to the traditional lecture format (134/232, 57.8%; P<.001).

CONCLUSIONS

Commercial medical education videos may enhance curriculum with low faculty effort and improve students' learning experiences. Findings from our experience at one medical school can guide the effective use of supplemental digital resources for learning, and related evaluation and research.

How to Use YouTube for Radiology Education

YouTube, the most commonly used free video-sharing platform globally, is increasingly being used as an educational tool in Radiology. Trainees worldwide now have the opportunity to learn about medical imaging at their own pace in the comfort of their homes, without geographical and financial constraints. Unfortunately, because YouTube is an easily accessible platform, it also incurs the risk of disseminating erroneous medical information or low-quality educational content. This article outlines the primary considerations when creating educational content on YouTube, including technical aspects, best practices, and measures to maximize effectiveness and success. Additionally, we discuss the current usage of the platform for Radiology education and its advantages and disadvantages and list some of the most popular Radiology YouTube channels.

Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques

Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.

Becoming Clinician-Animators: a Toolkit and Pilot Study for Novel Animated Content Development in a Medical Education Curriculum

Animation is increasingly incorporated into multimedia teaching tools in medical education. Despite enthusiasm about animation among learners and educators, evidence is limited and conflicting regarding the effectiveness of animation in medical education. In this how-to guide and pilot study, a team of clinician-educators have cataloged their efforts to learn the art of animation and understand how to most effectively integrate animation into a flipped classroom curriculum. In this pilot, internal medicine residents responded that an animated video series using anthropomorphic characters and metaphorical dialogue and narrative was an accessible, acceptable, and effective method of teaching the antihyperglycemic medications for type 2 diabetes mellitus. This study paves a path for further exploration of the intersection between multimedia, entertainment, and cognitive theory in graduate medical education.

Blended learning of radiology improves medical students' performance, satisfaction, and engagement

Purpose

To evaluate the impact of blended learning using a combination of educational resources (flipped classroom and short videos) on medical students’ (MSs) for radiology learning.

Material and methods

A cohort of 353 MSs from 2015 to 2018 was prospectively evaluated. MSs were assigned to four groups (high, high-intermediate, low-intermediate, and low achievers) based on their results to a 20-MCQs performance evaluation referred to as the pretest. MSs had then free access to a self-paced course totalizing 61 videos based on abdominal imaging over a period of 3 months. Performance was evaluated using the change between posttest (the same 20 MCQs as pretest) and pretest results. Satisfaction was measured using a satisfaction survey with directed and spontaneous feedbacks. Engagement was graded according to audience retention and attendance on a web content management system.

Results

Performance change between pre and posttest was significantly different between the four categories (ANOVA, P = 10⁻⁹): low pretest achievers demonstrated the highest improvement (mean ± SD, + 11.3 ± 22.8 points) while high pretest achievers showed a decrease in their posttest score (mean ± SD, − 3.6 ± 19 points). Directed feedback collected from 73.3% of participants showed a 99% of overall satisfaction. Spontaneous feedback showed that the concept of “pleasure in learning” was the most cited advantage, followed by “flexibility.” Engagement increased over years and the number of views increased of 2.47-fold in 2 years.

Conclusion

Learning formats including new pedagogical concepts as blended learning, and current technologies allow improvement in medical student’s performance, satisfaction, and engagement.

Efficacy of Instagram in Promoting Psychoeducation in the Chinese-Speaking Population

Purpose: To evaluate the efficacy of the use of Instagram in disseminating information regarding first-episode psychosis and schizophrenia.

Methods: Facebook and Instagram advertisements linked to an external YouTube video detailing first-time psychosis were initiated for 48 h. Metrics regarding the number of unique individuals reached and number of engagements were collected. Descriptive statistics were used to analyze the data.

Results: Facebook made 85 impressions (32.82%) and Instagram made 174 impressions (67.18%). Facebook had 24 engagements, whereas Instagram had 42.

Conclusion: Instagram is noninferior to Facebook in disseminating psychoeducational material to the Chinese-speaking population.

Consulting "Dr. YouTube": an objective evaluation of hypospadias videos on a popular video-sharing website

INTRODUCTION

Parents who make decisions about hypospadias repair for their child may seek information from online platforms such as YouTube.

OBJECTIVE

The purpose of this study is to evaluate the health literacy demand of hypospadias videos on YouTube using the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V).

STUDY DESIGN

We performed a YouTube search using the term "hypospadias," limiting results to the first 100 videos. We excluded videos that were <1 min or >20 min and videos that were not in English or did not include subtitles. Two evaluators independently examined videos and determined PEMAT-A/V scores for understandability and actionability (i.e., ability to identify actions the viewer can take). Videos with scores >70% are understandable or actionable. The inter-rater reliability (kappa) and intraclass correlation coefficient (ICC) of PEMAT scores were calculated. Bivariate and multivariable linear regression models assessed the association of video characteristics with respective scores.

RESULTS

Of the 100 videos that were identified on YouTube, 47 (47%) were excluded leaving 53 for analysis: 14 were >20 min, 14 were <1 min, 9 had no audio or subtitles, 7 were not in English, 1 was a duplicate, 1 was unrelated to hypospadias, and 1 was deleted at the time of data analysis. Three (5.6%) were understandable (mean score 54.5%, standard deviation (SD) 14.9) and eight (15.1%) were actionable (mean score 21.8%, SD 16.6) (Extended Summary Figure). Kappa values ranged from 0.4 to 1. The ICC's were 0.55 and 0.33 for understandability and actionability, respectively. In the bivariate analysis, mean understandability scores were significantly higher for English language videos (p = 0.04), videos with animation (p = 0.002), and those produced by industry (p = 0.02). In the multivariable analysis, mean understandability scores were significantly higher for "expert testimonial" or "other" video types after adjusting for graphics type and overall tone (p = 0.04). Mean understandability scores were also significantly higher for videos with animation after adjusting for video type and overall tone (p = 0.01). Mean actionability scores were significantly higher for videos with a negative tone (p = 0.01).

DISCUSSION

The vast majority of hypospadias-related YouTube content is not appropriate for users with low health literacy although certain types of videos, such those with animation and expert testimonials, scored higher on understandability than other types.

CONCLUSION

Due to the lack of sufficient online informational content regarding hypospadias, we plan to engage parents of sons with hypospadias in the development of high-quality patient educational materials about hypospadias.

Strategic Considerations for Applying the Flipped Classroom to Neurology Education

Nowadays, the "flipped classroom" approach is taking the center stage within medical education. However, very few reports on the implementation of the flipped classroom in neurology have been published to date, and this educational model still represents a challenge for students and educators alike. In this article, neurology educators from the American Academy of Neurology's A. B. Baker Section on Neurological Education analyze reports of flipped classroom in other medical/surgical subspecialties, review the current implementation in neurology, and discuss future strategies to flip the neurology curriculum through contextualization of the benefits and the consequences. ANN NEUROL 2020;87:4-9.

Epilepsy Education: Recent Advances and Future Directions

PURPOSE OF REVIEW

The goal of this review is to survey the current literature on education in epilepsy and provide the most up-to-date information for physicians involved in the training of future doctors on this topic. We intended to review what opportunities exist to enhance our current teaching practices that may not be well-known or widely used, but may be adapted to a broader audience.

RECENT FINDINGS

Many new techniques adopting principles of education (e.g., retrieval practice and spaced learning) or new technologies (e.g., pre-recorded lectures, computer-enhanced modules, and simulation practice) have been trialled to enhance medical education in epilepsy with some success. Many of these techniques are currently adaptable to a wider audience or may soon be available. The use of these opportunities more broadly may allow expansion of educational research opportunities as well as enhancing our ability to pass on information. As the knowledge base in epilepsy continues to dramatically expand, we need to keep evaluating our teaching techniques to ensure we are able to pass along this knowledge to our future providers.