Evidence map›Paper›PMID 38982322›Full record

ArticleBMC medical education2024

Application of data-driven blended online-offline teaching in medicinal chemistry for pharmacy students: a randomized comparison.

Yong-Ming Zhao, Si-Si Liu, Jin Wang

Abstract readComparative Study
In one paragraph

Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yong-Ming ZhaoDepartment of Pharmacy, Hebei North University, Zhangjiakou, China.
Si-Si LiuDepartment of Pharmacy, Hebei North University, Zhangjiakou, China.
Jin WangDepartment of Pharmacy, Hebei North University, Zhangjiakou, China. wangjinzym@163.com.

Funding

Hebei Provincial Higher Eduction Teaching Reform and Practice Project 2021GJJG343
6 · The paper itself

Abstract

backgroundThe purpose of this study was to evaluate the effectiveness and efficiency of implementing a data-driven blended online-offline (DDBOO) teaching approach in the medicinal chemistry course.

methodsA total of 118 third-year students majoring in pharmacy were enrolled from September 2021 to January 2022. The participants were randomly assigned to either the DDBOO teaching group or the traditional lecture-based learning (LBL) group for medicinal chemistry. Pre- and post-class quizzes were administered, along with an anonymous questionnaire distributed to both groups to assess students' perceptions and experiences.

resultsThere was no significant difference in the pre-class quiz scores between the DDBOO and LBL groups (T=-0.637, P = 0.822). However, after class, the mean quiz score of the DDBOO group was significantly higher than that of the LBL group (T = 3.742, P < 0.001). Furthermore, the scores for learning interest, learning motivation, self-learning skill, mastery of basic knowledge, teamwork skills, problem-solving ability, innovation ability, and satisfaction, as measured by the questionnaire, were significantly higher in the DDBOO group than in the traditional group (all P < 0.05).

conclusionThe DDBOO teaching method effectively enhances students' academic performance and satisfaction. Further research and promotion of this approach are warranted.

Indexed as

Chemistry, PharmaceuticalEducational MeasurementEducation, PharmacyStudents, PharmacyComputer-Assisted InstructionCurriculumEducation, DistanceFemaleHumansMaleSurveys and QuestionnairesYoung AdultBlended teachingData-drivenLecture-based learningMedicinal chemistryOnline-offline teachingPBL

Identifiers

PMID38982322
PMCPMC11232250

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.