effectiveness of blended learning versus lectures alone on ecg … · 2020. 12. 3. · medical...

RESEARCH ARTICLE Open Access

Effectiveness of blended learning versuslectures alone on ECG analysis andinterpretation by medical studentsCharle André Viljoen1,2,3* , Rob Scott Millar1,2, Kathryn Manning2 and Vanessa Celeste Burch2

Abstract

Background: Most medical students lack confidence and are unable to accurately interpret ECGs. Thus, bettermethods of ECG instruction are being sought. Current literature indicates that the use of e-learning for ECG analysisand interpretation skills (ECG competence) is not superior to lecture-based teaching. We aimed to assess whetherblended learning (lectures supplemented with the use of a web application) resulted in better acquisition andretention of ECG competence in medical students, compared to conventional teaching (lectures alone).

Methods: Two cohorts of fourth-year medical students were studied prospectively. The conventional teachingcohort (n = 67) attended 4 hours of interactive lectures, covering the basic principles of Electrocardiography,waveform abnormalities and arrhythmias. In addition to attending the same lectures, the blended learning cohort(n = 64) used a web application that facilitated deliberate practice of systematic ECG analysis and interpretation,with immediate feedback. All participants completed three tests: pre-intervention (assessing baseline ECGcompetence at start of clinical clerkship), immediate post-intervention (assessing acquisition of ECG competence atend of six-week clinical clerkship) and delayed post-intervention (assessing retention of ECG competence 6 monthsafter clinical clerkship, without any further ECG training). Diagnostic accuracy and uncertainty were assessed in eachtest.

Results: The pre-intervention test scores were similar for blended learning and conventional teaching cohorts(mean 31.02 ± 13.19% versus 31.23 ± 11.52% respectively, p = 0.917). While all students demonstrated meaningfulimprovement in ECG competence after teaching, blended learning was associated with significantly better scores,compared to conventional teaching, in immediate (75.27 ± 16.22% vs 50.27 ± 17.10%, p < 0.001; Cohen’s d = 1.58),and delayed post-intervention tests (57.70 ± 18.54% vs 37.63 ± 16.35%, p < 0.001; Cohen’s d = 1.25). Althoughdiagnostic uncertainty decreased after ECG training in both cohorts, blended learning was associated with betterconfidence in ECG analysis and interpretation.

(Continued on next page)

© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

* Correspondence: [email protected] of Cardiology, Groote Schuur Hospital, Faculty of Health Sciences,University of Cape Town, Observatory, Cape Town 7925, South Africa2Department of Medicine, Groote Schuur Hospital, Faculty of HealthSciences, University of Cape Town, Observatory, Cape Town 7925, SouthAfricaFull list of author information is available at the end of the article

Viljoen et al. BMC Medical Education (2020) 20:488 https://doi.org/10.1186/s12909-020-02403-y

http://crossmark.crossref.org/dialog/?doi=10.1186/s12909-020-02403-y&domain=pdf

http://orcid.org/0000-0001-7246-4136

http://creativecommons.org/licenses/by/4.0/

http://creativecommons.org/publicdomain/zero/1.0/

mailto:[email protected]

(Continued from previous page)

Conclusion: Blended learning achieved significantly better levels of ECG competence and confidence amongstmedical students than conventional ECG teaching did. Although medical students underwent significant attrition ofECG competence without ongoing training, blended learning also resulted in better retention of ECG competencethan conventional teaching. Web applications encouraging a stepwise approach to ECG analysis and enablingdeliberate practice with feedback may, therefore, be a useful adjunct to lectures for teaching Electrocardiography.

Keywords: Blended learning, Electrocardiography, Medical students

BackgroundThe incorrect interpretation of an electrocardiogram(ECG) may lead to inappropriate clinical decisionswith adverse outcomes [1, 2]. Although computerisedECG diagnostic algorithms are available, these are fre-quently not accurate and clinicians should thereforenot rely on these automated ECG interpretations [3–6]. ECG interpretation is thus an essential learningoutcome in undergraduate medical curricula [7, 8].The concern is that medical students around theworld lack competence and confidence in ECG ana-lysis and interpretation [9–14]. For this reason, it isimportant to review the way that Electrocardiographyhas been conventionally taught.With the widespread availability of computers and

the Internet, contemporary health professions’ educa-tion increasingly uses e-learning to supplementclassroom-based teaching such as lectures [15, 16]. InElectrocardiography, computer-assisted instruction(CAI) dates back to the 1960’s when analogue com-puters were used to teach ECGs to medical students[17]. However, since the turn of the millennium, web-based learning has been increasingly used as amethod of ECG instruction [18]. Recent work hasshown that an online programme facilitating repeatedECG interpretation with deliberate practice and feed-back enhanced learning [19]. Although web-basedlearning has previously been shown to be at least aseffective as conventional methods of instruction inhealth sciences [20], e-learning on its own has notconclusively been shown to be more effective thanlecture-based training for the acquisition of ECG ana-lysis and interpretation skills (hereafter referred to asECG competence) [18]. A sub-analysis of this meta-analysis showed that blended learning (face-to-facelectures complemented by e-learning) [21] had a posi-tive impact on the acquisition of ECG competence.However, the effectiveness of blended learning on theretention of ECG competence remains unknown [18].The aim of our study was therefore to compare the ef-

fectiveness of blended learning (combination of face-to-face lectures and e-learning) to conventional ECG teach-ing (face-to-face lectures only) on the acquisition and re-tention of ECG competence of medical students (Fig. 1).

MethodsStudy design and participantsThis prospective study, conducted from January 2016 toNovember 2017, included two cohorts of fourth year med-ical students at the University of Cape Town, South Af-rica. Medical students were recruited during their InternalMedicine clinical clerkship, during which Electrocardiog-raphy is traditionally taught. Students recruited in 2016formed the conventional teaching cohort, whereas stu-dents from 2017 formed the blended learning cohort.

Method of ECG instructionAt the University of Cape Town, medical students areintroduced to the basic principles of Electrocardiographyduring their third year of study, when they attend aseries of lectures introducing rhythm and waveform ab-normalities. Therefore, all participants in this study hadprior exposure to ECG teaching in the preceding aca-demic year. Training in Electrocardiography continuesduring the fourth-year Internal Medicine clinical clerk-ship in the form of lectures. Over and above lectures,the clinical clerkship also requires of students to acquireand analyse ECGs on the patients that they see. Theypresent patients and their ECGs to senior clinicians onward rounds. Although a year apart, both cohorts com-pleted the same Internal Medicine clinical clerkship,with the same clinicians, and the same learning require-ments and opportunities, both during lectures and onward rounds. There is no formal ECG training after theInternal Medicine clinical clerkship. The other fourth-year clerkships at our institution comprise Obstetrics,Neonatology, Psychiatry and Public Health.During the study period, participants attended two lec-

tures (of 120min each) at the end of the second andfourth week of the Internal Medicine Clerkship respect-ively. The lectures revisit the basic principles of ECGanalysis (including calculation of the heart rate, measur-ing the different intervals and calculating the QRS axis),and then predominantly focus on waveform abnormal-ities (e.g. left and right atrial enlargement, left and rightventricular hypertrophy [LVH, RVH], left and right bun-dle branch block [LBBB, RBBB], left anterior fascicularblock, Wolff-Parkinson-White [WPW] pattern, ST-segment elevation myocardial infarction [STEMI],

Viljoen et al. BMC Medical Education (2020) 20:488 of 16

pericarditis, hyperkalaemia, long QT syndrome) andrhythm abnormalities (e.g. sinus arrhythmia, sinus arrestwith escape rhythm, first degree atrioventricular [AV]block, Mobitz I and II second degree AV block, third de-gree AV block, atrial fibrillation [AF] with normal anduncontrolled rates, atrial flutter, AV node re-entry tachy-cardia [AVNRT], ventricular tachycardia [VT] and ven-tricular fibrillation [VF]). The topics included in thesyllabus are considered core knowledge for undergradu-ate ECG training at our Institution [22]. All ECGs were12-lead ECGs, with the exception of VF for which achest lead rhythm strip was shown. The lectures wereinteractive, i.e. students were asked to analyse andinterpret all the ECGs shown during the Microsoft®PowerPoint® presentation, and they were encouraged toask questions. All lectures were facilitated by the samelecturer, who used the same Microsoft® PowerPoint®presentations (demonstrating the same ECG examplesand illustrations) throughout the study. Depending onthe student allocations to clinical clerkships, thegroup sizes varied between 32 and 42 students.Both cohorts received the same lectures. In addition,

the blended learning cohort had access to a web applica-tion (ECG ONLINE, accessed at ecgonline.uct.ac.za),

which facilitated ECG analysis and interpretation withfeedback. Access was free, but restricted to Universitystaff and registered students at the time of the study.Use of the web application was voluntary; its use wasnot a compulsory learning activity during the clerkship.Once signed into the web application, users had accessto five online modules, each containing four to six ECGsto analyse. The ECGs used in the web application weredifferent to the ECGs used during the lectures, but wereof the same diagnoses discussed in class. For each of the24 ECGs contained in the web application, the user wasprovided with a standardised template for online ana-lysis, as shown in Supplementary Material 1. The tem-plate contained checkboxes for normal and abnormalparameters, as well as textboxes for interval measure-ments and axis calculations. For each ECG, the user se-lected the checkboxes that were relevant to the ECGanalysis and entered the values of the interval measure-ments and axis calculations. The web application re-quired that users analysed the rate and rhythm beforeproceeding to the detailed waveform analysis, and priorto providing their interpretation (diagnosis) of the ECG[23]. Once the process of ECG analysis and interpret-ation were complete and submitted, users were provided

Fig. 1 Outline of study design


http://ecgonline.uct.ac.za

with the correct answers on the same page to facilitatecomparison with the answers they had provided. Foreach analysed ECG, the user could also download adocument with a take-home message (with text andannotated ECGs), as shown in Supplementary Mater-ial 2. There was no limit to the number of times thatparticipants could analyse the ECGs. Students couldalso review their previous analyses, along with the an-swers of all their previously submitted ECGs. Theweb application monitored the number of ECGs ana-lysed by each user.The features offered by the web application used in

this study, as well as the ECG curriculum taught on-line, are summarised in Table 1. These aspects werecompared to undergraduate ECG teaching softwarethat has previously been described in the literature

[18, 24–33], and assessed by the modified Kirkpatrickframework [34].

Assessment of ECG competenceThe study flow of participants and competence tests isoutlined in Fig. 1. During the study, participants wereasked to complete three 30-min competence tests. Eachcomprised 28 single best answer multiple-choice ques-tions (MCQ). The first MCQ test (pre-intervention test)was written on enrolment, i.e. in the first week of the In-ternal Medicine clinical clerkship, prior to any ECGteaching in that academic year, to determine baseline(pre-existing) ECG competence. The second MCQ test(immediate post-intervention test) was written at theend of the six-week clinical clerkship, after ECG tuition(with or without access to e-learning in the blended

Table 1 Comparison of the web application used in this study (ECG ONLINE) and undergraduate ECG teaching software previouslydescribed in the literature

Author Viljoen Akbarzadeh Chudgar Davies Fent Montassier Nilsson Patuwo Rui Sonali

Reference number Indexstudy

[24] [25] [26] [27] [28] [29] [30] [31] [32]

Web application features (how ECGs were taught)

Online lecture / video X X

Online text / images X X X X X X X ?

Systematic analysis (step by step approach) X X

Practice / quiz with feedback X X X X X X X

Case scenarios X X X X

Simulation X X

Online chat rooms X

ECG curriculum on web application (what was taught)

Basic principles / pathophysiology X X X X X X X X X

Normal ECG X X X

Normal / Abnormal rhythms X X X X ? X

Normal / Abnormal waveforms X X X X X ? X

Educational approach

Blended learning (e-learning in addition tolectures)

X X X X

Unrestricted access X X X X

Evaluation of web application according to the modified Kirkpatrick framework

Level 1: Participants’ reactions X X X X X X X X

Level 2a: Modifications of attitudes andperceptions

X X X X

Level 2b: Acquisition of knowledge and skills X X X X X X X X X X

Assessment of ECG competence

Immediately after educational intervention(acquisition of ECG competence)

X X X X X X X X X X

Delayed testing after educationalintervention

(retention of ECG competence)

X X

X = described by authors? = not specifically mentioned, but implied by the text


learning and conventional teaching cohorts respectively),to assess the participants’ acquisition of ECG compe-tence. The third MCQ test (delayed post-interventiontest) was written 6 months later, without any furtherECG training, or access to the web application, to assessthe participants’ retention of ECG competence. The first,second and third MCQ tests respectively were the samefor both cohorts, i.e. the two cohorts underwent thesame assessment of baseline ECG competence, as well asacquisition and retention of ECG competence.The three tests examined the same topics, using the

same multiple-choice questions and answers, but withdifferent exemplar ECGs in the three respective tests.Each test included three questions regarding basic ECGanalysis (i.e. calculating the rate, measuring the QRSwidth and determining the QRS axis), as well as 25 ECGdiagnoses. Of these, 12 were rhythm abnormalities (sinusarrhythmia, sinus arrest with escape rhythm, first degreeAV block, Mobitz I and II second degree AV block, thirddegree AV block, AF with normal and uncontrolledrates, atrial flutter, AVNRT, VT and VF), and 13 werewaveform abnormalities (left and right atrial enlarge-ment, LVH, RVH, LBBB, RBBB, left anterior fascicularblock, WPW pattern, anterior and inferior STEMI, peri-carditis, hyperkalaemia, long QT syndrome). These con-ditions that were included in the MCQ tests areconsidered core knowledge for the undergraduate ECGtraining at our Institution [22]. The ECGs used in thetests were not the same as those used in class or on theweb application. Two cardiologists and two specialistphysicians, with a special interest in Electrocardiography,agreed that the ECGs used in the tests were unequivocalexamples of the conditions and that the multiple-choiceoptions were fair for the given ECG.The MCQ tests were administered electronically at the

University computer laboratories. They were invigilated,password-protected and could only be accessed on theday of the test. The order in which the questions wereasked was randomised. For each question, there werefive optional answers - four possible diagnoses (of whichonly one was correct), and a fifth option, i.e. “I am notsure what the answer is”. Each correct question wasawarded one mark and negative marking was not ap-plied. The answers to the questions were only madeavailable to the students at the end of the study. The re-sults of the MCQ tests in this study did not contributeto the participants’ course mark.

Survey of confidence in ECG interpretationAfter the immediate post-intervention test, participantscompleted a survey in which they were asked to ratetheir confidence in ECG analysis and interpretationusing 5-point Likert-type questions (to which the partici-pants could select strongly agree, agree, neutral, disagree

or strongly disagree) [35, 36]. For purposes of analysis,the responses were clustered into three categories (agree,neutral and disagree).

Determining other learning materials used during studyperiodAfter the immediate post-intervention test, participantswere also asked to declare which learning materials (i.e.textbooks, class notes) they used during the studyperiod.

Students’ perception of lectures and web-based learningParticipants were asked to comment on what they likedand what they disliked of the lectures (both the conven-tional teaching and blended learning cohorts) and webapplication (blended learning cohort only). The feedbackwas received in free-text form. Two investigators (CAV,VCB) performed qualitative content analysis of the feed-back from the participants. An inductive approach wasused to identify themes and subthemes from the free-text comments made by the participants with regards tothe lectures and web application [37, 38]. The themesand subthemes were refined through an iterative processof reviewing the participants’ responses [39]. Disagree-ment was resolved through discussions with a third in-vestigator (RSM). A deductive approach was used toquantify the frequency in which the themes and sub-themes emerged from the feedback on the lectures andweb application [40].

Estimated sample size needed for an adequately poweredstudyWe estimated that a minimum sample size of 36 partici-pants in each group would provide 80% power to detecta mean difference of 10% in the test scores after inter-vention between the two groups and considering an α(type 1 error) of 5%. This calculation was based on theresults of previous studies that compared blended learn-ing (lectures complemented by CAI) to lectures alonefor teaching Electrocardiography [18, 25, 29, 31].

Eligibility to be included in the studyAll fourth-year medical students were invited to takepart in the study; participation was voluntary. Partici-pants were only included if they completed all threeMCQ tests and the survey on ECG confidence duringthe study period.

Statistical analysisStatistical analyses were performed on anonymised datausing Stata (Version 14.2, StataCorp, College StationTX, USA). The Shapiro-Wilk test was used to assess dis-tributional normality of data [41]. Parametric data weresummarised as means with standard deviations (SD),


whereas median with interquartile range (IQR) wereused for non-parametric data. Paired and unpaired t-tests were used to assess within-group and between-group differences in test scores respectively. Cohen’s dwas used to determine the effect size (practical signifi-cance) of the differences in test scores, with values of0.2, 0.5 and 0.8 indicating small, moderate and large ef-fect sizes respectively. Categorical variables wereexpressed as frequencies and percentages. Chi-squaredor Fisher’s exact tests were used, where applicable, tocompare categorical variables. A p value of < 0.05 wasconsidered statistically significant.

ResultsStudy populationAll fourth-year medical students were invited to partici-pate in the study. The conventional teaching cohort con-sisted of 67 of the 86 students from the 2016 class,whereas the blended learning cohort comprised 64 of 98students from the 2017 class.

Use of ECG learning material during the study periodAll students in the blended learning cohort accessedthe web application. Of the 24 ECGs on the web ap-plication, the median number of ECGs that were ana-lysed and interpreted was 24 (IQR 21–24). Afterhaving analysed all the ECGs they had access to, al-most two-thirds (64.2%) of the participants analysedat least one of the 24 ECGs more than once. Asdepicted in Fig. 2, the web application was used

throughout the day, but the peak times were aroundmidday and early evening. Those who had access tothe online modules used textbooks less often than thegroup who attended lectures only (31.3% vs 56.1%,p = 0.003). However, both groups made similar use oftheir class notes to study ECGs (68.6% vs 71.6%, p =0.717).

Baseline ECG competenceAs shown in Table 2, the cohorts exposed to blendedlearning or conventional teaching started with similarbaseline ECG competence (mean pre-intervention testscores of 31.0% ± 13.2% and 31.2 ± 11.5% respectively,p = 0.917).

Acquisition of ECG competenceBoth cohorts showed a significant improvement in ECGcompetence after 6 weeks of training (Fig. 3). The con-ventional teaching cohort demonstrated a 1.6-fold in-crease in the mean test scores (31.2 ± 11.5% [pre-intervention test] to 50.3 ± 17.1%, p < 0.001 [immediatepost-intervention test]; Cohen’s d = 1.3 [95% CI 0.9,1.6]), whereas a 2.4-fold improvement in mean testscores was observed in the cohort exposed to theblended learning strategy (31.0% ± 13.2% to 75.3 ± 16.2%,p < 0.001; Cohen’s d = 3.1 [95% CI 2.6, 3.6]). The differ-ence in acquisition of competence test scores betweenthe two cohorts was also highly significant (Cohen’s d =1.5 [95% CI 1.1, 1.9]). These test performance

Fig. 2 The web application was accessed throughout the day, but peaked around midday and early evening


improvements were observed for basic analysis, as wellas for the interpretation of rhythm and waveform abnor-malities in the immediate post-intervention test (Table2).

Retention of ECG competenceAfter 6 months of no further ECG training, both cohortsdemonstrated attrition of ECG competence (Table 2,Fig. 3). ECG competence declined significantly betweenthe immediate and delayed post-intervention tests in theconventional teaching cohort (50.3 ± 17.1% to 37.6 ±16.4%, p < 0.001, Cohen’s d = − 0.8 [95% CI -1.1, − 0.4]).Of note is that the delayed post-intervention test scorein the conventional teaching cohort was similar to thatof their pre-intervention test score (31.2 ± 11.5% vs37.6 ± 16.4%, p < 0.001, Cohen’s d = 0.4 [95% CI 0.1,0.8]). The attrition of ECG competence between the im-mediate and delayed post-intervention tests was also sig-nificant in the blended learning cohort (mean score of75.3 ± 16.2% to 57.7 ± 18.5%, p < 0.001, Cohen’s d = − 1.0[95% CI -1.4, − 0.6]). However, in this cohort of students,ECG competence at 6 months remained almost twice asmuch as their initial performance in the baseline test(57.7 ± 18.5% vs 31.0% ± 13.2%, p < 0.001, Cohen’s d = 1.7[95% CI 1.3, 2.1]). Indeed, the blended learning cohort’s

performance in the delayed post-intervention test wasbetter than that achieved by the students who receivedconventional teaching immediately post-intervention(mean score of 57.7 ± 18.5% vs 50.3 ± 17.1%). Again, sig-nificant differences in test scores were observed for basicanalysis, as well as for the interpretation of rhythm andwaveform abnormalities between the two cohorts in thedelayed post-intervention test.

Decreased diagnostic uncertainty after trainingAs shown in Table 3, during the pre-intervention test,the “I am not sure what the answer is” option (indicatingdiagnostic uncertainty) was selected by the blendedlearning cohort as the response for 27.5% of the ques-tions. In the same test, participants who attended con-ventional lectures indicated diagnostic uncertainty for22.1% of the submitted answers. For both cohorts, therewas a significant reduction in the diagnostic uncertaintyin the immediate and delayed post-intervention tests.Post-intervention analysis of the responses, for whichthere was initial diagnostic uncertainty, translated to asignificantly higher proportion of correct responses inthe blended learning cohort versus the cohort that onlyattended lectures. This effect was still observed after 6months.

Table 2 The effect of blended learning versus conventional teaching on ECG competence

Conventional teachingMean % (SD)

Blended learningMean % (SD)

P value *

Total test score (28 test items)

Pre-intervention test 31.2 (11.5) 31.0 (13.2) 0.917

Immediate post-intervention test 50.3 (17.1) 75.3 (16.2) < 0.001

Delayed post-intervention test 37.6 (16.4) 57.7 (18.5) < 0.001

Basic analysis (3 tests items) †


Immediate post-intervention test 57.2 (25.2) 68.6 (22.9) 0.007


Rhythm abnormalities (12 tests items) ‡




Waveform abnormalities (13 tests items) §




SD Standard deviation*Unpaired T test of the difference in the mean scores between the cohorts exposed to blended learning and conventional teaching respectively†Calculating the QRS rate, measuring the QRS width and determining the QRS axis‡Sinus arrhythmia, sinus arrest with escape rhythm, first degree AV block, Mobitz type I second degree AV block, Mobitz type II second degree AV block, third degree AVblock, atrial fibrillation with uncontrolled rate, atrial fibrillation with normal rate, atrial flutter, AV node re-entry tachycardia, ventricular tachycardia andventricular fibrillation§Left anterior fascicular block, left bundle branch block, right bundle branch block, left atrial enlargement, right atrial enlargement, left ventricular hypertrophy, rightventricular hypertrophy, anterior ST-segment elevation myocardial infarction (STEMI), inferior STEMI, pericarditis, hyperkalaemia and long QT syndrome


Fig. 3 Learning and attrition with blended learning versus conventional teaching

Table 3 The effect of blended learning versus conventional teaching on diagnostic uncertainty and accuracy

Conventional teaching(n = 67)

Blended learning(n = 64)

Diagnostic uncertainty a

Pre-intervention test 415 / 1876 (22.1%) 492 / 1792 (27.5%)

Immediate post-intervention test 133 / 1876 (7.1%) 69 / 1792 (3.9%)

Delayed post-intervention test 121 / 1876 (6.4%) 120 / 1792 (6.7%)

Diagnostic accuracy for items for which there was diagnostic uncertainty in the pre-intervention test

Immediate post-intervention test 168 / 415 (40.5%) b 360 / 492 (73.2%) c

Delayed post-intervention test 113 / 415 (27.2%) b 251 / 492 (51.0%) c

aDiagnostic uncertainty was calculated as the percentage of “I do not know the answer” responses that were selected by each cohort in the 28 item ECGcompetence testsbBased on the 415 responses for which participants in the conventional teaching cohort (N = 67) selected “I do not know the answer” in the pre-intervention ECGcompetence testcBased on the 492 responses for which participants in the blended learning cohort (N = 64) selected “I do not know the answer” in the pre-intervention ECGcompetence test


Students’ confidence and competence at ECG analysisand interpretationA comparison of participants’ perceptions of their confi-dence and their measured competence in ECG analysis andinterpretation, is shown in Table 4. Students exposed toblended learning reported less difficulty in ECG analysisand interpretation and performed significantly better in theimmediate post-intervention test. Sub-group analysesshowed that students who undertook blended learningdemonstrated high levels of confidence and competence inrhythm and waveform analysis. While students whoattended conventional teaching reported high levels of con-fidence in rhythm analysis, this was not associated withhigh levels of diagnostic accuracy of arrhythmias. Thesestudents did, however, lack confidence in waveform ana-lysis, which was mirrored by significantly poorer perform-ance compared to students exposed to blended learning.

Students’ perception of ECG lecturesAs summarised in Table 5, students from both the conven-tional teaching and blended learning cohorts found the

lectures to be interactive and contextualised. Overall, stu-dents complimented the systematic, stepwise approach toECG analysis and interpretation that was taught during thelectures. They liked that difficult concepts were simplifiedand that they were taught to understand mechanisms caus-ing waveform and rhythm abnormalities, instead of merelymemorising patterns. Whereas some students felt that therewas insight to their level of understanding, others reportedthat the lecturer did not gauge whether they understoodconcepts in class or not. Although both cohorts liked thepractice of ECG analysis under supervision of a lecturer,the conventional cohort in particular pointed out that therewas not enough opportunity to practice in class. Thosefrom the blended learning cohort reported that the com-bination of lectures and the web application was beneficialfor their learning, because they could apply their know-ledge. However, the students often appeared overwhelmedby the lectures, saying that there was “too much informationcovered in one go”. They did not appreciate late afternoonlectures or attending ECG lectures only midway into theclinical clerkship. The blended learning cohort, in

Table 4 The effect of blended learning versus conventional teaching on confidence and competence in ECG analysis andinterpretation

Confidence Competence

Statements used toexpress confidence

Conventionalteaching(n = 67)

Blendedlearning(n = 64)

Pvalue

Test items used to measure competence Conventionalteaching(n = 67)

Blendedlearning(n = 64)

P value

Overall ECG analysis and interpretation

“I find ECG analysis andinterpretation difficult”

53.7% 18.8% <0.001

Basic analysis (QRS rate, width and axis) andrhythm and waveform abnormalities a

50.3% 75.2% < 0.001

Sub-analysis of rhythm abnormalities

“I am confident inanalysing an ECG withbradycardia”

91.0% 93.8% 0.560 Sinus arrest with escape rhythm, Mobitz type Iand II second degree AV block, third degree AVblock, hyperkalaemia

41.2% 75.6% < 0.001

“I am confident inanalysing an ECG withtachycardia”

89.6% 84.4% 0.378 Atrial fibrillation with uncontrolled rate, atrialflutter, AV node re-entry tachycardia, ventriculartachycardia

45.8% 57.3% 0.021

“I am confident inanalysing an ECG withan irregular rhythm”

56.7% 62.5% 0.500 Sinus arrhythmia, Mobitz type I and II seconddegree AV block, atrial fibrillation withcontrolled and uncontrolled rate

50.4% 77.3% < 0.001

Sub-analysis of waveform abnormalities

“I am confident inanalysing the P wave”

64.1% 93.8% <0.001

Left and right atrial enlargement 58.2% 84.4% < 0.001

“I am confident inanalysing the PRinterval”

49.3% 64.1% 0.087 First degree AV block, Mobitz type I seconddegree AV block, third degree AV block

44.8% 81.3% < 0.001

“I am confident inanalysing the QRSmorphology”

9.0% 56.3% <0.001

Left and right bundle branch block, Wolff-Parkinson-White pattern, left and right ventricu-lar hypertrophy

53.2% 77.1% < 0.001

“I am confident inanalysing the STsegment”

41.8% 67.2% 0.004 Anterior and inferior ST-segment elevationmyocardial infarction (STEMI), pericarditis

46.2% 78.6% < 0.001

aSinus arrhythmia, sinus arrest with escape rhythm, first degree atrioventricular (AV) block, Mobitz I and II second degree AV block, third degree AV block, atrialfibrillation with controlled and uncontrolled rates, atrial flutter, AV node re-entry tachycardia, ventricular tachycardia and ventricular fibrillation, left and right atrialenlargement, left and right ventricular hypertrophy, left and right bundle branch block, left anterior fascicular block, Wolff-Parkinson-White pattern, anterior and inferiorST-segment elevation myocardial infarction (STEMI), pericarditis, hyperkalaemia, long QT syndrome


particular, criticised the lectures as being too long and cov-ering too much content. However, the cohort who onlyattended lectures commented more often that the lectureswere too few and too sporadic. The cohort that receivedconventional teaching reported that the lectures consistedof too many students, there was a lack of opportunity topractise ECG analysis in class and students were afraid toparticipate in front of their peers (Table 6).

Students’ perception of online ECG learningTables 7 and 8 respectively depict what the blended learn-ing cohort liked and disliked about the web application.These students commented favourably on the fact that theweb application allowed for practice and revision of ECGanalysis and interpretation, and that it facilitated asyn-chronous learning (i.e. whenever or wherever convenient,at their own pace). The most important features that werepositively perceived were the systematic approach that wastaught online, the take-home messages that could be

downloaded for every ECG that they analysed and the im-mediate feedback received after they analysed and inter-preted the ECG. However, the web application wascriticised because, although it displayed whether the ana-lysis was correct or incorrect and provided the correct an-swer, it did not indicate or explain why an answer wasincorrect in the case of an incorrect submission. Althoughsome students felt that the web application exposed themto a wide variety of ECGs, others reported that they wouldhave liked more examples of each condition. Technical as-pects such as web page layout and the intermittent ‘bugsand glitches’ were also criticised.

DiscussionThis study evaluated the effectiveness of using a blendedlearning strategy (i.e. conventional lectures supple-mented with the use of a web application) and comparedresults to conventional teaching (i.e. lectures alone) ofECG analysis and interpretation skills in fourth year

Table 5 Themes and subthemes of what participants liked about the lectures

Theme Subtheme Numberof mentions

Example of feedback

CT BL

Method ofinstruction

Systematic / stepwise approach 14 19 “Breakdown of ECGs such that there is a step by step approach”“Teaching was delivered in a systematic way that is easy to reproduceand most of the time leads you to the correct diagnosis”

Interactive 12 9 “Able to ask questions and receive an answer”“I was also afforded an opportunity to ask questions if I didn’tunderstand”

Supervised practice 3 2 “We got to participate in interpreting ECG with the supervision of askilled clinician”

Well-paced delivery 2 3 “Taught at a pace that I could follow”

Simplifying concepts 13 10 “They made hard concepts more simple”

Taught to understand mechanisms insteadof memorising patterns

1 6 “The explanations of why patterns/ changes arise in certain conditionshelped me to remember them”“If I understand how the condition works, I find that I am able to retainthe information”

Visual orientation 0 3 “I liked that it was very visual”

Contextualised 3 3 “Use of real life ECGs as examples”

Scaffolded learning 4 0 “It allowed me to build on my previous knowledge of ECG’s”

Blended learning N/A 18 “I enjoyed the combination of the online component to the ECG lectureteaching as I got to apply my knowledg.”

Lecturerattributes

Insight to level of student understanding 4 4 “It seemed like he understood what we could possibly misunderstandand catered for it in his explanations”

Enthusiasm 2 3 “It was enthusiastically taught”

Skilled teacher 3 1 “Great presenting and explanation”

Engagement 0 2 “I enjoyed the way … teaches and his willingness to explain to students”

Learningenvironment

Safe learning space 1 1 “The lecturers were conducted in a student friendly manner and createda safe learning space”

Clinical application 0 1 “I enjoyed being able to actually interpret ECGs and use them as a toolto diagnose patients”

BL Blended learning; CT Conventional teaching; N/A Not applicable


Table 6 Themes and subthemes of what participants disliked about the lectures

Theme Subtheme Numberof mentions

Example of feedback

CT BL

Lecture time Too long 5 14 “Lectures were too long. They should have been broken up into 3 or 4 shortersessions”

Too short 15 6 “I did not like the fact that we had to learn such a crucial skill in such a shortamount of time”

Too few 10 5 “Sporadic”, “Too few”

End of day 2 4 “Time slots in afternoon for ECG teaching can be tiring and I tended to losefocus at times during the teaching”

Too late in clerkship 0 4 “I would have loved to have had [ECG teaching] earlier during the rotation”

Method ofinstruction

Content overload 5 8 “Too much information was covered in one go, therefore making it difficult todigest the information given”

Lack of opportunity to practice inclass

8 1 “The information was not retained because we couldn’t practice”

Focus on summative assessment 1 0 “ECG teaching at the moment is very much for exam and test purposes”

Material not available forpreparation

2 1 “Although it would have been more helpful if we had access to the notesbefore and after in order to read up beforehand”

Large group instruction 3 0 “There were too many of us present”

Lecturerattributes

Lecturer failed to gauge if studentsunderstood content

3 0 “Probing for understanding was not done in class”

Studentattributes

Afraid of participation in front ofpeers

4 0 “Sometimes you are scared to ask questions because you don’t know how tophrase your confusion”

Attrition of knowledge 4 0 “At the time, they made sense, but I forgot many things”

BL Blended learning; CT Conventional teaching; N/A Not applicable

Table 7 Themes and subthemes of what participants liked about the web application

Theme Subtheme Number ofmentions

Example of feedback

Method of instruction Systematic /stepwise approach

30 “It helped me develop a method of approaching ECGs more systematically”“The fact that it takes you through an ECG in a step wise process - it teaches you amethod”

Deliberate practice 5 “it is possible to practice lots of ECG interpretations”

Immediatefeedback

18 “I loved … the fact that you get immediate feedback on answers”“The best part was having such immediate feedback … I learnt from my mistakesstraight away without wondering where I went wrong or forgetting to follow up with alecturer”

Downloadablenotes on ECGs

24 “The take home message diagrams and pictures were so useful in understanding and notjust rote learning information”“I liked the take home messages. They were explained in a way that encouraged thinkinginto the pathology behind the pattern and not just pattern recognition”

Liberal use ofexamples

7 “It has taught me a lot of things about different ECGs”“The wide variety of ECGs”

Revision 3 “Being able to review ECGs you have previously analysed”

Asynchronouslearning opportunities

Learning at ownpace

4 “I could learn on my own at my own pace”

Accessible whereconvenient

3 “Being able to access the tool off-campus”

Accessible whenconvenient

2 “It is something that you can access at anytime”

Features of webapplication

User friendly 8 “Well-structured and easy to follow”

Web page layout 2 “I enjoyed the layout of the ECG online website”

Zoom function 1 “The ability to zoom easily on ECGs”


medical students. We found that blended learning wasassociated with greater confidence and competence inECG analysis and interpretation skills, immediately afterthe educational intervention. These gains were evidentin basic ECG analysis (i.e. calculating the heart rate,QRS width and axis), as well as in the interpretation ofrhythm and waveform abnormalities. Whilst there wasattrition of ECG competence in both groups after 6months of no further ECG teaching, those exposed toblended learning retained significantly more ECG ana-lysis and interpretation skills than their counterpartswho only attended lectures. Although diagnostic uncer-tainty decreased with both blended learning and conven-tional ECG teaching, those who engaged in blendedlearning activities had significantly better diagnostic ac-curacy in the immediate and delayed post-interventiontests for topics for which they initially reported diagnos-tic uncertainty.There are various possible reasons for the superior

gains in ECG competence with a blended learning strat-egy. First and foremost, blended learning potentially in-creases the time that students spend on ECG learning.Many have argued that current undergraduate medicaltraining does not allow sufficient time for this activity [7,8, 42, 43]. In our study, students reported that theyfound lectures to be too short and sporadic. This was es-pecially true for those from the conventional teachingcohort. In this regard, a blended learning strategy offersthe benefit of additional training by means of a web ap-plication, without increasing face-to-face ECG tuitiontime [15, 28]. The benefit of supplementing lectures withe-learning had been shown to be most significant whenstudents had unrestricted access to computer-assistedECG instruction [18].As shown in this study, an important benefit of e-

learning is that it allows for asynchronous learning, be-cause students can study the online material whereverand whenever convenient, in addition to attending face-

to-face teaching [9, 28, 44, 45]. We also showed thatECG learning on the web application took placethroughout the day, but peaked at midday and earlyevening, at which time students do not have lectures.Asynchronous e-learning supports the self-directedlearning theory in which learners self-regulate theirlearning by planning and monitoring their learning [46–49]. Self-directed learning allows for repetitive practiceand focused revision of learning material [19, 50–52],which has been shown to be of benefit. This was true inour study, as participants commented that both lecturesand web application taught a systematic approach toECG analysis and interpretation, but that the onlineplatform allowed them the opportunity to practise andconsolidate these diagnostic approaches through repeti-tion. With self-directed and asynchronous e-learning,students can adjust the pace of their learning and spendas much time as they need to assimilate new knowledge[24, 25, 53], which is a major advantage of blendedlearning over conventional classroom ECG teaching.This was consistent with the feedback from the partici-pants in this study, as the lectures were criticised for be-ing too rushed and covering too much content in toolittle time, whereas with the web application they couldstudy at their own pace.In our study, students who undertook blended learn-

ing had the opportunity to practise ECG analysis onlineand receive immediate feedback. As one participantpointed out “The best part was having such immediatefeedback … I learnt from my mistakes straight awaywithout wondering where I went wrong or forgetting tofollow up with a lecturer”. These self-administered on-line quizzes filled the gap of limited opportunity forpractice and feedback during lectures, especially in thelarge group setting [42, 54]. Indeed, those who onlyattended lectures pointed out the lack of opportunity topractise ECG analysis in class more often. This is an im-portant drawback of large group teaching [55].

Table 8 Themes and subthemes of what participants disliked about the web application

Theme Subtheme Number ofmentions

Example of feedback

Method of instruction ECG analysis too detailed 9 “Too many options to choose from. Overwhelming”

ECG analysis laborious 6 “It took a long time to complete one ECG”

Inadequate feedback 7 “I did not like the fact that, the feedback only showed thecorrect answers and there was no explanation on how to get the answers”

No point of reference 2 “There was no place on the website that you could go backand look if you did not know what the morphology looked like”

Limited number ofexamples

2 “I wish there were more examples”

Features of webapplication

Bugs and glitches 10 “There were some glitches when it wouldn’t work”

Web page layout 13 “I think the layout can be improved a bit more”


Deliberate practice with feedback, which underpins re-flective models of learning, has previously been shownto enhance learning [52, 56, 57], and improve the reten-tion of knowledge [58, 59]. This is also true of the initialacquisition of ECG competence [18, 25, 28, 29, 60]. Thefindings of our study further support these observationsin the literature. In addition, we show that blendedlearning using a web application providing immediatefeedback was also associated with better retention ofECG competence 6 months after the learning activities.This has not previously been reported [18].Our study confirms the ‘learning and forgetting curve’

described in the literature, where competence increaseswith training, but declines without ongoing teaching[61] and testing [62]. In this study, all students experi-enced attrition of ECG competence in the absence ofongoing ECG training. The attrition rate was the sameover time amongst those who engaged in blended learn-ing and those who attended lectures during their clerk-ship. After 6 months of no further ECG training, bothcohorts retained 75% of the ECG competence gained bythe respective educational interventions. However, theretention of ECG analysis and interpretation skills wassignificantly better in the blended learning group. Thiscould potentially be explained by the higher level ofECG competency initially achieved by this group duringthe clinical clerkship.In other domains of medicine, such as dermatology

and radiology, where diagnosis also depends on a visualanalysis, it has been shown that diagnostic uncertaintydecreases with experience [63–66]. Our work confirmsthat this is true for Electrocardiography as well, whetherconventional or blended learning strategies are used.However, students who engaged in blended ECG learn-ing activities achieved better diagnostic accuracy fortopics for which there was initial diagnostic uncertainty.In addition to the lack of ECG competence, under-

graduate and postgraduate students also lack confi-dence in ECG analysis and interpretation, whichimproves with training [14]. In this study we observedthat medical students exposed to blended learningwere more confident in ECG analysis and interpret-ation at the end of their clinical clerkship. This confi-dence matched their competence in basic ECGanalysis and the interpretation of abnormal rhythmsand waveforms. However, for students who were ex-posed to conventional ECG teaching alone, there wasa dissociation between their self-reported confidenceand competence in rhythm analysis.The development of web-based learning material has

the potential of being expensive and time-consuming forboth lecturer and student. Although the creation of edu-cational content requires the experience of the medicaleducator, the creation and maintenance of online

platforms on which the educational content is ultimatelyhosted requires information technology (IT) skill and ex-pertise, which might not necessarily be available at allteaching institutions [45, 67]. The implication is there-fore that funding should be sought for the developmentand hosting of online material, which may be an add-itional burden to Faculty budgets. Although ECG ON-LINE was offered to our students at no cost, the expenseof development and hosting of the web application todate is estimated at $30,000. To make online learning fi-nancially sustainable, students are often required to paysubscriptions to access web applications. The creation ofnew material also requires the time of lecturers, who areoften clinicians with busy practices. However, once cre-ated and accessible to students, it could offer unlimitedcontact time with online material. The deliberate prac-tice with feedback on web applications is not limited tothe time and availability of the lecturer. Lecturersshould, however, recognise the danger of curricularoverload [22, 68], and consider whether their students’studying schedules would allow for additional learningmaterial.Our research has practice implications. On the basis of

our study, we have implemented blended learning usinga web application that facilitates deliberate practice ofECG analysis and interpretation with feedback. Whetherthis strategy will benefit graduating medical studentsand ultimately improve patient care remains to be stud-ied. However, an additional challenge for educators willbe to further improve retention of ECG competencethrough continuous exposure in a longitudinal curricu-lum [62].

Study limitationsIn a study with different cohorts, there is a possibility ofperformance bias, i.e. exposure to factors other than theeducational intervention that may have influenced theoutcomes among the different groups. Although the twocohorts reviewed ECGs with the same senior clinicianson ward rounds, it is impossible to control teaching op-portunities during a clinical clerkship.We acknowledge different participation rates in the

two cohorts. This is most likely due to a change in theorder of the fourth-year clinical clerkships at the Univer-sity during the study period. Participants in the blendedlearning cohort had to travel from another hospital totake part in the retention of knowledge tests, whereasthe conventional teaching cohort was already on thesame campus as the computer laboratories on the day ofthe retention test. However, the number of eligible par-ticipants and the results of the pre-intervention tests didnot differ between the cohorts.It was beyond the scope of this study to evaluate how

the students engaged with the online material. Such an


appraisal would be important to improve future e-learning interventions and blended learning strategies,and should be studied in future.

ConclusionOur study found that a blended learning strategy re-sulted in better acquisition of ECG competence thanlectures alone. As expected, diagnostic uncertainty de-creased with both teaching modalities. However,blended learning was associated with better diagnosticaccuracy in situations where there was initial diagnos-tic uncertainty. Whilst there was an attrition of ECGanalysis and interpretation skills without further train-ing, the six-month retention of ECG competence wasbetter amongst those who were exposed to blendedlearning. Blended learning using a web applicationthat facilitates deliberate practice of systematic ECGanalysis with feedback may, therefore, be a usefuladdition to the learning toolbox for Electrocardiog-raphy training of medical students.

Glossary terms

� ‘Blended learning’ refers to a combination oflectures and e-learning [69].

� ‘Computer-assisted instruction’ (CAI) refers toany teaching method that uses a digital platform as aself-directed learning technique [23].

� ‘E-learning’ refers to the teaching method wherebyelectronic technologies (such as web applications)are used to access learning material [69].

� ‘ECG analysis’ refers to the detailed examination ofan ECG tracing, which requires the measurement ofintervals and the evaluation of the rhythm and eachwaveform [23].

� ‘ECG interpretation’ refers to the conclusionreached after careful ECG analysis, that is, making adiagnosis of an arrhythmia, ischaemia and so on[23].

� ‘ECG competence’ refers to the ability to accuratelyanalyse and interpret the ECG [23].

� ‘ECG knowledge’ refers to the understanding ofECG concepts, for example, knowing thattransmural ischaemia or pericarditis can cause ST-segment elevation [23].

Supplementary InformationThe online version contains supplementary material available at https://doi.org/10.1186/s12909-020-02403-y.

Additional file 1: Supplementary Material 1. The user was providedwith a short clinical vignette and ECG with a standardised template foronline analysis.

Additional file 2: Supplementary Material 2. Example of a ‘take-home message’ that could be downloaded from the web applicationonce the ECG was analysed and interpreted.

AbbreviationsAF: Atrial fibrillation; AV: Atrioventricular; AVNRT: Atrioventricular nodal re-entry tachycardia; ECG: Electrocardiogram; ICTS: Information andcommunication technology services; IQR: Interquartile range; LBBB: Leftbundle branch block; LVH: Left ventricular hypertrophy; RBBB: Right bundlebranch block; RVH: Right ventricular hypertrophy; SD: Standard deviation;STEMI: ST-segment elevation myocardial infarction; UCT: University of CapeTown; VF: Ventricular fibrillation; VT: Ventricular tachycardia; WPW: WolffParkinson White

AcknowledgementsThe authors wish to thank Dr. Rachel Weiss, Professor Mpiko Ntsekhe andProfessor Ashley Chin for their important role in the development of ECGONLINE. We wish to thank Jason Sheldon for creating and maintaining ECGONLINE during the study period, and the ICTS at the University of CapeTown for hosting the web application. We are grateful for the advice andguidance from Francois Oosthuizen and the generous support of theResearch Contracts and Innovations Office and the University of Cape Town,who through various research and teaching grants, have made thedevelopment and improvement of ECG ONLINE possible. We would like tothank Mr. Charle Viljoen for analytical support and Dr. Julian Hövelmann, Dr.Nicholas Simpson and Ms. Sylvia Dennis for their excellent help in themanuscript preparation. We are grateful to the academic staff, Professor NicciWearne, Dr. Ayanada Gcelu, Ms. Sipho Mankayi, Ms. Zanele Magwa and Ms.Janine Daniels, for their assistance with the lectures and invigilating at theECG assessments. Finally, we would like to thank our students for theirdedication in taking part in the study and their very valuable feedback tohelp improve undergraduate ECG training.

Authors’ contributionsCAV conceived the study protocol, with advice from RSM and VCB regardingstudy design. CAV collected the data. CAV performed the statistical analysisunder the guidance of KM. CAV, RSM and VCB interpreted the results. CVdrafted the manuscript, which was critically revised by RSM, KM and VCB. Allauthors have read and approved the final version of the manuscript.

Authors’ informationCV, MBChB MMed FCP (SA), is a fellow in Cardiology at Groote SchuurHospital and a PhD student at the University of Cape Town.RSM, MBChB FCP (SA), is an emeritus associate professor at the University ofCape Town. He continues to play a key role in postgraduate ECG teaching inSouth Africa.KM, BS PG Dip (Diet) MSc (Med), is a statistical analyst at the University ofCape Town.VB, MBChB FCP (SA) MMed PhD, is an honorary professor at the University ofCape Town. She has a leading position in curricular design for postgraduateteaching in South Africa.

FundingECG ONLINE (ecgonline.uct.ac.za) was developed by the Clinical Skills Centrein collaboration with the Division of Cardiology and Department of Medicineat the University of Cape Town. Development of ECG ONLINE was funded bya Teaching Development Grant and the Clinical Skills Centre at the Universityof Cape Town. Access to the web application was free.

Availability of data and materialsThe datasets used and/or analysed during the current study, are available inthe “Effectiveness of blended learning versus lectures alone on ECG analysisand interpretation by medical students” repository, which could be accessedat https://doi.org/10.25375/uct.12931262.v1.

Ethics approval and consent to participateApproval was obtained from the Human Research Ethics Committee (HREC)at the Faculty of Health Sciences (HREC reference number 764/2015), as wellas institutional permission from the Department of Student Affairs at the


https://doi.org/10.1186/s12909-020-02403-y

https://doi.org/10.1186/s12909-020-02403-y

https://doi.org/10.25375/uct.12931262.v1

University of Cape Town. All participants signed informed consent prior toenrolment into the study.

Consent for publicationStudy participants provided written informed consent for the anonymisedanalysis of their ECG competence tests and a possible scientific publicationof the results.

Competing interestsThe authors report no conflicts of interest. None of the authors receivedremuneration from ECG ONLINE. The authors alone are responsible for thecontent and writing of the article.

Author details1Division of Cardiology, Groote Schuur Hospital, Faculty of Health Sciences,University of Cape Town, Observatory, Cape Town 7925, South Africa.2Department of Medicine, Groote Schuur Hospital, Faculty of HealthSciences, University of Cape Town, Observatory, Cape Town 7925, SouthAfrica. 3Hatter Institute for Cardiovascular Research in Africa, Faculty of HealthSciences, University of Cape Town, Observatory, Cape Town 7925, SouthAfrica.

Received: 7 June 2020 Accepted: 24 November 2020

References1. Bogun F, Anh D, Kalahasty G, Wissner E, Bou Serhal C, Bazzi R, et al.

Misdiagnosis of atrial fibrillation and its clinical consequences. Am J Med.2004;117(9):636–42.

2. Masoudi FA, Magid DJ, Vinson DR, Tricomi AJ, Lyons EE, Crounse L, et al.Implications of the failure to identify high-risk electrocardiogram findingsfor the quality of care of patients with acute myocardial infarction: results ofthe emergency department quality in myocardial infarction (EDQMI) study.Circulation. 2006;114(15):1565–71.

3. Schlapfer J, Wellens HJ. Computer-interpreted electrocardiograms: benefitsand limitations. J Am Coll Cardiol. 2017;70(9):1183–92.

4. Mant J, Fitzmaurice DA, Hobbs FD, Jowett S, Murray ET, Holder R, et al.Accuracy of diagnosing atrial fibrillation on electrocardiogram by primarycare practitioners and interpretative diagnostic software: analysis of datafrom screening for atrial fibrillation in the elderly (SAFE) trial. BMJ. 2007;335(7616):380.

5. Guglin ME, Thatai D. Common errors in computer electrocardiograminterpretation. Int J Cardiol. 2006;106(2):232–7.

6. Hurst JW. The interpretation of electrocardiograms: pretense or a well-developed skill? Cardiol Clin. 2006;24(3):305–7 vii.

7. Jablonover RS, Lundberg E, Zhang Y, Stagnaro-Green A. Competency inelectrocardiogram interpretation among graduating medical students.Teach Learn Med. 2014;26(3):279–84.

8. Breen C, Zhu T, Bond R, Finlay D, Clifford G. The evaluation of an opensource online training system for teaching 12 lead electrocardiographicinterpretation. J Electrocardiol. 2016;49(3):454–61.

9. Rolskov Bojsen S, Rader SB, Holst AG, Kayser L, Ringsted C, HastrupSvendsen J, et al. The acquisition and retention of ECG interpretation skillsafter a standardized web-based ECG tutorial-a randomised study. BMC MedEduc. 2015;15:36.

10. Blissett S, Cavalcanti R, Sibbald M. ECG rhythm analysis with expert andlearner-generated schemas in novice learners. Adv Health Sci Educ TheoryPract. 2015;20(4):915–33.

11. Kopec G, Magon W, Holda M, Podolec P. Competency in ECG interpretationamong medical students. Med Sci Monitor. 2015;21.

12. Lever NA, Larsen PD, Dawes M, Wong A, Harding SA. Are our medicalgraduates in New Zealand safe and accurate in ECG interpretation? N ZMed J. 2009;122(1292):9–15.

13. Little B, Mainie I, Ho KJ, Scott L. Electrocardiogram and rhythm stripinterpretation by final year medical students. Ulster Med J. 2001;70(2):108–10.

14. McAloon C, Leach H, Gill S, Aluwalia A, Trevelyan J. Improving ECGcompetence in medical trainees in a UK district general hospital. CardiolRes. 2014;5(2):51–7.

15. Choules AP. The use of elearning in medical education: a review of thecurrent situation. Postgrad Med J. 2007;83(978):212–6.

16. Vallée A, Blacher J, Cariou A, Sorbets E. Blended learning compared totraditional learning in medical education: systematic review and meta-analysis. J Med Internet Res. 2020;22(8):e16504.

17. Owen S, Hall R, Waller I. Use of a teaching machine in medical education;preliminary experience with a programme in electrocardiography. PostgradMed J. 1964;40(460):59.

18. Viljoen CA, Scott Millar R, Engel ME, Shelton M, Burch V. Is computer-assisted instruction more effective than other educational methods inachieving ECG competence amongst medical students and residents? Asystematic review and meta-analysis. BMJ Open. 2019;9(11):e028800.

19. Hatala R, Gutman J, Lineberry M, Triola M, Pusic M. How well is each learnerlearning? Validity investigation of a learning curve-based assessmentapproach for ECG interpretation. Adv Health Sci Educ Theory Pract. 2019;24(1):45–63.

20. Cook DA, Levinson AJ, Garside S, Dupras DM, Erwin PJ, Montori VM.Internet-based learning in the health professions: a meta-analysis. Jama.2008;300(10):1181–96.

21. Morton CE, Saleh SN, Smith SF, Hemani A, Ameen A, Bennie TD, et al.Blended learning: how can we optimise undergraduate studentengagement? BMC Med Educ. 2016;16:195.

22. Viljoen CA, Millar RS, Manning K, Burch VC. Determining electrocardiographytraining priorities for medical students using a modified Delphi method.BMC Med Educ. 2020;20(1):431.

23. Viljoen CA, Scott Millar R, Engel ME, Shelton M, Burch V. Is computer-assisted instruction more effective than other educational methods inachieving ECG competence among medical students and residents?Protocol for a systematic review and meta-analysis. BMJ Open. 2017;7(12):e018811.

24. Akbarzadeh F, Arbat BK, Alizadeh A, Akbarzadeh AH. The efficacy of web-based multimedia education of normal electrocardiogram in junior andsenior medical students. Res Dev Med Educ. 2012;1(2):77–9.

25. Chudgar SM, Engle DL, Grochowski CO, Gagliardi JP. Teaching crucial skills:an electrocardiogram teaching module for medical students. JElectrocardiol. 2016;49(4):490–5.

26. Davies A, Macleod R, Bennett-Britton I, McElnay P, Bakhbakhi D, Sansom J. E-learning and near-peer teaching in electrocardiogram education: arandomised trial. Clin Teach. 2016;13(3):227–30.

27. Fent G, Gosai J, Purva M. A randomized control trial comparing use of anovel electrocardiogram simulator with traditional teaching in theacquisition of electrocardiogram interpretation skill. J Electrocardiol. 2016;49(2):112–6.

28. Montassier E, Hardouin JB, Segard J, Batard E, Potel G, Planchon B, et al. E-learning versus lecture-based courses in ECG interpretation forundergraduate medical students: a randomized noninferiority study. Eur JEmerg Med. 2016;23(2):108–13.

29. Nilsson M, Bolinder G, Held C, Johansson BL, Fors U, Ostergren J. Evaluationof a web-based ECG-interpretation programme for undergraduate medicalstudents. BMC Med Educ. 2008;8:25.

30. Patuwo T, Wagner G, Ajijola O, editors. Comparison of teaching basicelectrocardiographic concepts with and without ECGSIM, an interactiveprogram for electrocardiography. Comput Cardiol. 2007;34:61−4.

31. Rui Z, Lian-Rui X, Rong-Zheng Y, Jing Z, Xue-Hong W, Chuan Z. Friend orfoe? Flipped classroom for undergraduate electrocardiogram learning: arandomized controlled study. BMC Med Educ. 2017;17(1):53.

32. Sonali N, Limaye RP, Madhushree K, Gokhale DV, Neeta G, Patil TR. Assessingimpact of computer assisted learning (cal) on cognitive perception -a studyin medical college students. Res J Pharm Biol Chem Sci. 2014;5(4):600–4.

33. Pontes PAI, Chaves RO, Castro RC, de Souza EF, Seruffo MCR, Frances CRL.Educational software applied in teaching electrocardiogram: a systematicreview. Biomed Res Int. 2018;2018:8203875.

34. Yardley S, Dornan T. Kirkpatrick's levels and education 'evidence. Med Educ.2012;46(1):97–106.

35. Boone HN, Boone DA. Analyzing likert data. J Ext. 2012;50(2):1-5.36. Norman G. Likert scales, levels of measurement and the "laws" of statistics.

Adv Health Sci Educ Theory Pract. 2010;15(5):625–32.37. Hashemnezhad H. Qualitative content analysis research: A review article. J

ELT Appl Linguist. 2015;3(1).38. Tavakol M, Sandars J. Quantitative and qualitative methods in medical

education research: AMEE guide no 90: part I. Med Teach. 2014;36(9):746–56.39. Tavakol M, Sandars J. Quantitative and qualitative methods in medical

education research: AMEE guide no 90: part II. Med Teach. 2014;36(10):838–48.


40. Walling A, Istas K, Bonaminio GA, Paolo AM, Fontes JD, Davis N, et al.Medical student perspectives of active learning: a focus group study. TeachLearn Med. 2017;29(2):173–80.

41. Shapiro SS, Wilk MB. An analysis of variance test for normality (completesamples). Biometrika. 1965;52(3/4):591–611.

42. Fent G, Gosai J, Purva M. Teaching the interpretation of electrocardiograms:which method is best? J Electrocardiol. 2015;48(2):190–3.

43. Hurst JW. Current status of clinical electrocardiography with suggestions forthe improvement of the interpretive process. Am J Cardiol. 2003;92(9):1072–9.

44. Greenhalgh T. Computer assisted learning in undergraduate medicaleducation. BMJ. 2001;322(7277):40–4.

45. Cook DA. Web-based learning: pros, cons and controversies. Clin Med. 2007;7(1):37–42.

46. Young JQ, Van Merrienboer J, Durning S, Ten Cate O. Cognitive load theory:implications for medical education: AMEE guide no. 86. Med Teach. 2014;36(5):371–84.

47. Neufeld VR, Barrows HS. The" McMaster philosophy": an approach tomedical education. Acad Med. 1974;49(11):1040–50.

48. Murray CJ, Barber RM, Foreman KJ, Abbasoglu Ozgoren A, Abd-Allah F,Abera SF, et al. Global, regional, and national disability-adjusted life years(DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for188 countries, 1990-2013: quantifying the epidemiological transition. Lancet.2015;386(10009):2145–91.

49. Taylor DC, Hamdy H. Adult learning theories: implications for learning andteaching in medical education: AMEE guide no. 83. Med Teach. 2013;35(11):e1561–e72.

50. Khogali S, Davies DA, Donnan P, Gray A, Harden RM, McDonald J, et al.Integration of e-learning resources into a medical school curriculum. MedTeach. 2011;33(4):311–8.

51. Custers EJFM. Long-term retention of basic science knowledge: a reviewstudy. Adv Health Sci Educ Theory Pract. 2010;15(1):109–28.

52. Issenberg SB, McGaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ. Featuresand uses of high-fidelity medical simulations that lead to effective learning:a BEME systematic review. Med Teach. 2005;27(1):10–28.

53. Owen SG, Hall R, Anderson J, Smart GA. Programmed learning in medicaleducation. An experimental comparison of programmed instruction byteaching machine with conventional lecturing in the teaching ofelectrocardiography to final year medical students. Postgrad Med J. 1965;41(474):201–5.

54. Cantillon P. Teaching large groups. BMJ. 2003;326(7386):437.55. Luscombe C, Montgomery J. Exploring medical student learning in the

large group teaching environment: examining current practice to informcurricular development. BMC Med Educ. 2016;16:184.

56. Cook DA, Levinson AJ, Garside S, Dupras DM, Erwin PJ, Montori VM.Instructional design variations in internet-based learning for healthprofessions education: a systematic review and meta-analysis. Acad Med.2010;85(5):909–22.

57. Ericsson KA, Krampe RT, Tesch-Römer C. The role of deliberate practice inthe acquisition of expert performance. Psychol Rev. 1993;100(3):363.

58. Larsen DP, Butler AC, Roediger HL 3rd. Test-enhanced learning in medicaleducation. Med Educ. 2008;42(10):959–66.

59. Ericsson KA. Deliberate practice and the acquisition and maintenance ofexpert performance in medicine and related domains. Acad Med. 2004;79(10):S70–81.

60. Barthelemy FX, Segard J, Fradin P, Hourdin N, Batard E, Pottier P, et al. ECGinterpretation in emergency department residents: an update and e-learning as a resource to improve skills. Eur J Emerg Med. 2017;24(2):149–56.

61. Pusic MV, Kessler D, Szyld D, Kalet A, Pecaric M, Boutis K. Experience curvesas an organizing framework for deliberate practice in emergency medicinelearning. Acad Emerg Med. 2012;19(12):1476–80.

62. Boutis K, Pecaric M, Carriere B, Stimec J, Willan A, Chan J, et al. The effect oftesting and feedback on the forgetting curves for radiograph interpretationskills. Med Teach. 2019;41(7):756–64.

63. Lawton R, Robinson O, Harrison R, Mason S, Conner M, Wilson B. Are moreexperienced clinicians better able to tolerate uncertainty and manage risks?A vignette study of doctors in three NHS emergency departments inEngland. BMJ Qual Saf. 2019;28(5):382–8.

64. Zwaan L, Hautz WE. Bridging the gap between uncertainty, confidence anddiagnostic accuracy: calibration is key. BMJ Qual Saf. 2019;28(5):352–5.

65. Lowenstein EJ, Sidlow R. Cognitive and visual diagnostic errors indermatology: part 1. Br J Dermatol. 2018;179(6):1263–9.

66. Kok EM, Jarodzka H, de Bruin AB, BinAmir HA, Robben SG, van MerrienboerJJ. Systematic viewing in radiology: seeing more, missing less? Adv HealthSci Educ Theory Pract. 2016;21(1):189–205.

67. Masters K, Ellaway R. E-learning in medical education guide 32 part 2:technology, management and design. Med Teach. 2008;30(5):474–89.

68. van den Berge K, van Gog T, Mamede S, Schmidt HG, van Saase JLCM,Rikers RMJP. Acquisition of visual perceptual skills from worked examples:learning to interpret electrocardiograms (ECGs). Interact Learn Environ. 2013;21(3):263–72.

69. Ellaway R, Masters K. AMEE guide 32: e-learning in medical education part 1:learning, teaching and assessment. Med Teach. 2008;30(5):455–73.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.


effectiveness of blended learning versus lectures alone on ecg … · 2020. 12. 3. · medical...

Documents