Application of data-driven blended online-offline teaching in medicinal chemistry for pharmacy students: a randomized comparison | BMC Medical Education

Participants

This teaching reform experiment is open to all third-year Pharmacy students at Hebei North University. Before commencing the experiment, students were required to complete a short screening questionnaire to ensure they had the necessary resource for the experiments. The questionnaire asked the following five yes-or-no questions: (1) Do you have a stable internet connection? (2) Do you have access to an independent electronic device (laptop, tablet, or smartphone)? (3) Are you able to complete the online course? (4) Are you able to complete the exams and questionnaires? (5) Are you aware of this experiment and willing to participate? Students who answered “yes” to all questions were eligible for the study, while those who answered “no” to one or more questions were excluded.

Sample size, grouping and blinding methods

According to the sample size calculation method reported in the literature [14], the study required a minimum of 52 participants per group to achieve a significance level (α) of less than 0.05 and a power (1-β) of 80%. The participants were randomly divided into experimental group (n = 59) and control group (n = 59) using a simple randomization. Both groups were supervised by the same teaching team, including one professor and two assistants. The experiment was conducted using a single-blind method and the students were blinded after assignment to interventions.

Study design

We have employed a randomized controlled trail to assess the effectiveness of a data-driven blended online-offline (DDBOO) teaching model on a group of healthy volunteers. The DDBOO method was implemented in the experimental group, while the control group received the traditional lecture-based learning (LBL).

Interventions

The DDBOO model for medicinal chemistry course

The DDBOO instructional process is structured into three phases: pre-class, in-class, and post-class. Through a seamless integration of synchronous and asynchronous learning, we have formulated a comprehensive DDBOO teaching approach, as illustrated in Fig. 1.

An overview of the study design

Before class

The teacher introduces the theme, characteristics and tasks of the lesson online, emphasizing the importance of the chapter and sparking student’s interest. Students engage in self-directed online learning tasks utilizing the SuperStarLearn software. They access and complete tasks at their own pace, view microlecture videos covering key topics, and subsequently undergo corresponding chapter tests. Following this, Problem-based learning (PBL) scenarios are introduced, encouraging collaborative teamwork to address PBL tasks. For those who do not complete assigned tasks, the learning alert system prompts them to do so. Teachers analyze online learning data, including the duration and frequency of student video views and chapter test accuracy, to identify common issues and pinpoint teaching challenges.

In class

During the class, teachers provide comprehensive explanations for commonly challenging issues and assess the learning outcomes through features such as quick response and in-class quizzes on the SuperStarLearn platform. Group discussions and collaborative thinking are encouraged to achieve a deeper understanding. Teachers also provide individualized guidance to address specific issues encountered by students during the learning process. By analyzing learning behaviors, such as participation in quick response and thematic discussions, as well as statistical data from in-class quizzes and assessments of group tasks, teachers can determine student engagement, personalized challenges, and learning effectiveness. This analysis enables teachers to intervene promptly, making adjustments to the teaching pace as necessary.

Post class

At the end of the class, the students completed a post-quiz and a questionnaire consisting of nine questions. Following the class, learning data retrieved from the SuperStarLearn Platform reports are used to distribute personalized assignments. By analyzing data such as assignment accuracy, teachers identified cognitive gaps and deviations among students. This information allows for targeted supplementation and correction in the subsequent class.

LBL method for medicinal chemistry course

In the control group, the same topics were presented through LBL. The lectures comprised two sessions, conducted once a week for 90 min each. During the class, the routine included the teacher explaining the learning objectives (5 min), delivering the content using PowerPoint slides (65 min), engaging in exercises (10 min), and participating in a class discussion or question-and-answer session (10 min). Students had the opportunity to participate in a question-and-answer session during the lecture, and discussions were encouraged if students wished to share their opinions or respond to their peers’ questions.

Outcome measurements

After obtaining informed consent, basic information about the participants, including age and gender, was collected. To evaluate students’ comprehension and application of knowledge, both groups underwent the same assessments, consisting of one pre-quiz and one post-quiz, each lasting 60 min and scored out of 100 points. Additionally, a questionnaire survey was administered at the end of the course to measure students’ self-perceived competence. The details of the questionnaire are presented in the Supplementary materials. This survey covered various aspects such as learning interest, targeted learning, motivation, self-learning skills, mastery of basic knowledge, teamwork abilities, problem-solving proficiency, and innovation capacity. Responses were rated using a 5-level Likert scale: 5 points for “strongly agreed,” 4 points for “agreed,” 3 points for “neutral,” 2 points for “disagreed,” and 1 point for ” strongly disagreed.” Furthermore, a survey on satisfaction with the teaching mode was conducted, with responses categorized into four levels: “Very Satisfied,” “Satisfied,” “Neutral,” and “Dissatisfied.” In order to maintain impartial responses, both quizzes and questionnaires were conducted anonymously, mitigating any potential influence, whether positive or negative, on the students.

Statistical analysis

A chi-squared test (symbolically represented as χ²) was employed to assess the discrepancy of count data. To compare two independent groups, the student t-test was utilized. Data were expressed as individual values and as mean ± standard deviation (SD). Statistical analysis was conducted using IBM SPSS statistics 20.0 software. The significance level (alpha) was set to 0.05, and p-values less than 0.05 were considered statistically significant.