Analysis on the Implementation Effect of Promoting Active Learning with Project-Based Learning

International Journal of Education (IJE) Vol.12, No.2, June 2024
DOI:10.5121/ije.2024.12205 45
ANALYSIS ON THE IMPLEMENTATION EFFECT OF
PROMOTING ACTIVE LEARNING WITH
PROJECT-BASED LEARNING
Zhan Ying
Zhejiang Institute of Communications, Hangzhou, China
ABSTRACT
Background
Given today's information deluge and the swift strides in artificial intelligence, foundational knowledge is
readily accessible online. The willingness to take the initiative to learn is obviously more important. At
present, project-based learning is widely promoted in the world, and China is no exception, and since
2016, Zhejiang, China, has promoted STEAM education as a promotion curriculum integration. An
important starting point for transforming the way you learn. Although project-based learning is widely
carried out, there is a lack of quantitative research on the effective organization and implementation of
project-based learning and the corresponding learning effects.Educators build and provide learners with
supportive learning resources and tailor guiding issues of project-based learning to provide a path to
transition from traditional direct teaching to a project-based active learning approach that encourages
students to be proactive and seek resources as needed.
Objectives
This study will use the database design curriculum as an example to implement project-based learning,
build project-based learning elements, and record students' learning activity data in project-based
learning activities, which has 45 students in a junior college in Zhejiang in 2023.
Methods
The study collected data from learning platforms that included various supportive learning resources,
including the number of online discussions, the number of tasks completed, video watch time data, chapter
learning repetition, the distribution and trend of chapter learning time, teacher surveys, and face-to-face
discussions and offline unsupervised learning.
Results and Conclusions
This lab takes database project-based learning as an example and evaluates whether learners can gain
real-world practical experience in new learning methods. The analysis of project-based learning outcomes
shows that the project-based active learning method enhances students' awareness of the importance of
database design, cultivates enthusiasm, and promotes active learning in learning, better cultivate students
who can navigate beyond basic knowledge points to embrace multidimensional inventive learning.
Experimental data shows that strong positive correlation between high-quality project-based learning and
student enthusiasm.

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KEYWORDS
Project-Based Learning; Supportive Learning Resources; Guiding Issues of Project-Based Learning;
Effect Size; Learning Effect.
1. INTRODUCTION
Berman believes that "project learning is an activity that allows students to create, verify,
improve, and create something" [1]
(Sally Berman, 2004). In the context of " project-based
learning using databases as an example ", students confront authentic database design challenges,
formulate questions rooted in real-world scenarios, and undergo learning through problem-
solving in the design phase. This method promotes problem-solving via a blend of individual
practice and collaborative interaction. With a comprehensive inquiry approach, involving
intricate real-world problems and meticulously crafted tasks, students immerse themselves in the
acquisition of database knowledge and skills. The caliber of database design has a direct bearing
on the quality of data governance within the database management system. Given that database
technology is intrinsically hands-on, delving into practical, project-based learning using
databases as an example facilitates students' engagement in practical application discovery,
fosters the formulation of database construction concepts, and cultivates profound learning during
the database design and creation stages.
Yet, the question arises: Is project-based learning using databases as an example truly
efficacious? Currently, there is a noticeable gap in quantitative studies on the effectiveness of
project-based learning in China. Student enhancements and achievements in database design can
be attributed to myriad factors. What are the genuine elements that markedly augment learning
outcomes and foster authentic learning?
When juxtaposing project-based learning with conventional pedagogical techniques, is there a
noticeable uptick in students' zeal for grasping database technology? Do students exhibit a more
expansive thought process and approach when faced with challenges? Does project-based
learning bolster students' capabilities in transferring and resolving hands-on issues? To elucidate
these queries, a well-structured experiment is imperative, aiming to delineate the requisite
support for future curricula grounded in project-based learning.
2. PROJECT-BASED LEARNING ELEMENTS OF DATABASE DESIGN
The essence of project-based learning using databases as an example revolves around "self-
directed learning" and "personalized learning". This learning paradigm confronts students with
tangible tasks, compelling them to independently forge solutions. Within this learning
framework, participants autonomously gather database information, assimilate core database
knowledge, decipher user requirements through iterative interpersonal engagements, and actively
design and deploy standardized databases.
Active learning through project-based learning incorporates several elements: learning objectives,
auxiliary learning resources, guiding questions, project-oriented tasks, and process evaluations [2]
(Zhan Ying, 2022).
In this methodology, educators establish learning aims anchored. They then curate and dispense
an array of supportive learning resources, spanning conventional textbooks, expansive online
learning networks, and authentic database user requirements scenarios. Notably, the online
learning ecosystem comprises micro-video compilations, benchmark database samples, and

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platforms dedicated to online research and collaborative learning. Predicated upon the user
scenarios chosen by the students, educators allocate segmented tasks for each phase of the
tailored database design, frame guiding questions, and allow students to spearhead the database
design trajectory. This dynamic is enhanced by educator-student interactions and evaluations of
the design's progress from both clients and instructors. Given the individualized nature of
learning, diverse learners may receive varying feedback based on their distinct outcomes. Should
the interim evaluations reveal areas for potential enhancement, students are empowered to
selectively delve into pertinent support resources. Through continuous guidance and probing
questions, students gradually assimilate the rigorous standards inherent in database design,
cultivating the dexterity to apply knowledge to real-world challenges. This project-centric
learning model metamorphoses the erstwhile unilateral instructional approach into a more
dynamic one. Here, students tailor their learning journey by cherry-picking resources that align
with their design progress, specific project themes, and evaluation feedback. This shift fosters a
more student-led learning experience, ensuring genuine and profound comprehension. The
interplay between various components in the project-based learning using databases as an
example blueprint is depicted in Figure 1. Pupils leverage these supplementary resources to fulfill
tasks related to database creation and maintenance. Instructors' guidance, while implicit, remains
instrumental in curating these resources. And, whenever students find themselves in need of
technical insights, prompt feedback is rendered, nudging learners towards their objectives
through iterative evaluation cycles.
The supportive learning resources feature exemplary database models, micro-video compilations
elucidating these models, sequentially organized subtasks possessing inherent logic, and a curated
set of guiding questions tailored for each subtask. As students navigate through these structured
task lists, they progressively cultivate an inherent understanding. When striving to accomplish the
objectives of project-based learning tasks, students nurture a transferable thought process, evolve
to independently reason, spark innovative higher-order cognition, and ultimately achieve their
learning goals.
To realize these learning objectives, students adhere to the foundational thread of project-based
tasks, addressing real-world challenges and engaging in sustained learning pursuits. The learning
assignments, paired with project-based tasks within the supportive resources, synchronize in a
cyclical, mutually reinforcing manner, propelling the consistent exploration and assimilation of
knowledge.
During project-based learning, should students grapple with challenges or dilemmas, the provided
resources offer viable solutions. Beyond conventional teaching tools, these supplementary
resources curate illustrative "tasks" on digital platforms, structured akin to course directories.
This "chain" is readily accessible, allowing students the autonomy to revisit and engage with
content at their discretion. This empowers them, sparking inspiration, urging them to pivot their
thought processes, and subsequently craft their unique solutions.

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Figure 1. The relationship diagram of each practical element included in the project-based learning design
using databases as an example.
3. DESIGN OF PROJECT-BASED LEARNING TASKS
Xia Xuemei believes: "The design of project-based learning is inseparable from the exploration
of the concept of knowledge, and it is the reconstruction and creation of the core knowledge of
the subject in the context [3]
(Xia Xuemei, 2018). When crafting project-based learning tasks,
primary attention is accorded to the core project learning task chain and the subsequent process of
project learning activities. The former delineates the sequence of subtasks that students must
navigate, driven by guiding questions, necessitating persistent exploration. The latter depicts the
student's engagement with supportive learning resources while accomplishing project-based
tasks. It encompasses the application, completion of each subtask, peer-assessment or evaluations
from educators and clients, and iterative refinement of tasks based on these evaluations.
Emphasizing the nexus between curriculum study and real-world contexts [4]
(Zhang Feng et al.,
2022), Xia Xuemei believes: "For teachers, the Chinese construction of project-based learning
means that in the learning situation, they can flexibly choose the appropriate project type and
design for themselves. Real problems that are meaningful to students” [5]
(Xia Xue Mei, 2020).
Consequently, tasks associated with database design in project-based learning should be concise
yet flexible, granting students the liberty to independently chart their course. Possible avenues
include the design of a tea traceability system database, graduation thesis topics, database design
of management systems, or the formulation and upkeep of a "factory material management"
database for material management systems [6]
(Zhan Ying et al., 2022).
4. DESIGN OF GUIDING ISSUES
Effectively integrating practical guiding problems is fundamental to project-based learning. By
consistently posing guiding issues, students are encouraged to delve deeper into database design
project learning, igniting their curiosity to explore database knowledge and engage in immersive
thinking. These questions not only generate solution pathways but also invigorate discussions,
inquiries, and investigations, motivating students to take an active role in database practices.
Such questions should be woven throughout the entirety of the project learning process. Their

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presence not only structures and propels the progression of project learning activities but also
ensures that a range of tasks and activities maintain internal coherence.
How should one formulate driven issues pertaining to database design? Which questions will
effectively spur students into proactive thinking?
4.1. Guiding Issues should be Designed Around Real Project Topics
Guiding issues should possess a genuine authenticity, anchoring them to real-world scenarios. By
grounding these questions in tangible database design projects drawn from everyday life, students
are introduced to project-based learning. The end results are functional databases tailored to
address real-world challenges. When these databases meet the needs of users and garner approval
from clients, students not only experience a heightened sense of achievement but also develop a
more profound interest in database technology.
Take, for instance, the design of a "factory material management" database. This sizable project,
rooted in practical application scenarios, offers students invaluable guidance throughout the
project learning journey.
4.2. The Guiding Issues should be a Key Question that can Trigger Students'
Independent Exploration and Promote Students' Problem Solving
Guiding issues should guide students in their analytical and decision-making processes, foster
self-awareness, and aid in the construction of a structured database knowledge system. Over time,
these questions serve as a foundation for cultivating a problem-solving methodology. For
instance, a teacher might prompt students with the following thought process:
In undertaking the extensive project of designing database, into which distinct tasks should the
project be segmented?
Database design can be delineated into six interconnected tasks: requirement analysis, conceptual
structure design, logical structure design, database physical design, database implementation, and
database operation and maintenance [2]
(Zhan Ying et al., 2022). Collectively, these tasks form a
cohesive task chain.
Regarding each specific task, students might ask themselves: Am I equipped to handle this task?
What additional knowledge do I need to address this challenge? Is collaboration with others
necessary?
4.3. The Guiding Issues can be a General Task Related to the Learning Objectives
of Each Stage
The formulation of guiding issues should strongly align with learning objectives. Such questions
need to be grounded in scientific rigor, enabling students to grasp the nuances of relational
databases. Through project learning, the goal is to foster an understanding of core database
principles and nurture holistic database expertise.
Consider the following examples:
How can we engage with customers in a cordial manner?

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What knowledge is essential to grasp the normalization standards of a database?
Which user categories can access specific database information?
What considerations are pivotal when mapping out storage strategies for diverse files within the
database?
How can we engineer designs that safeguard data file integrity?
How would you ensure the database remains secure?
4.4. Guiding Issues should be Challenging and Appropriate for Students
Guiding issues should be rooted in students' perspectives, drawing from their experiences. Their
complexity needs to strike a balance, not being overly simple yet not surpassing students' current
understanding. This ensures that students frame these questions based on their accumulated
knowledge. The process of analyzing, recognizing, and decision-making evolves into a
methodology for thinking and problem-solving, nurturing their innovation skills and guiding
them towards resolutions. Comprehensive guiding issues should span all phases of project-based
learning, aiding students in building a cohesive database knowledge structure.
For instance:
In the event of data loss from disk corruption, how would you retrieve and restore the database?
What methods can be employed to oversee and evaluate database performance?
If the database's query efficiency diminishes, how would you enhance its performance?
When users require additional functionalities, what is the strategy for planning and executing a
database upgrade?
5. PROCESS EVALUATION
In addressing the pivotal challenges of database design, the learner team independently gathers
textual resources like textbooks. They leverage supplementary learning tools offered via the
online platform, understand database design methodologies, and select design themes with real-
world applications in mind. Through an unceasing exploration of knowledge, learners cultivate
independent thought, immerse themselves in the database design and creation process, harness
their imagination, and pursue innovative solutions to tangible challenges.
Evaluators for the project-based learning process might include users, instructors, or the students
themselves. By evaluating learning outcomes at each database design stage, feedback is offered
on design plan inadequacies. This leads to the formulation of focused guiding questions, enabling
students to self-learn, refine their plans, and effectively complete project-based learning
assignments with both efficiency and quality. It is imperative for students to take the helm of
their learning journey in project-based learning. Given the distinct learning goals and outcomes
for each database phase, corresponding evaluators and assessment methods are crucial. Post
identification of core tasks and learning objectives, educators craft the evaluation method,
pinpointing the emphasis and prerequisites in the project-based learning phase. They then
communicate these to students, ensuring they grasp the standardized database design expectations
[2] (Zhan Ying, 2022).
Evaluation metrics form a vital roadmap for learning. Continuous assessment throughout project-
based learning must mirror the learning impact. This is achieved by sequentially assessing
subtasks in alignment with learning goals, weighing the content and criteria, and shaping the
corresponding evaluations. Assessments encompass students' questioning prowess, analytical
skills, comprehension of database's central concepts during the learning curve, and the

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congruence of the developed database with learning goals and user needs, all while furnishing
feedback.
6. ANALYSIS OF PROJECT-BASED LEARNING EFFECTS
When contrasting direct teaching with project-based learning using databases as an example, how
effective is the latter? Does it foster the development of experts’ adept at addressing real-world
database issues? Database design operates as a large-scale project learning activity. Assessing the
outcomes of database project learning hinges not only on the attainment of the learning objective
but also on the quality of the final database produced.
The direct teaching approach was implemented in 2022. Essential supportive learning resources
tailored for project-based learning was established, paving the way for the adoption of the
project-based active learning methodology in 2023. After a year-long experimental teaching
phase, subsequently presented is a comparative analysis of the pre-test outcomes versus the post-
test results of the two pedagogical methods. The statistical comparison table of the
comprehensive situation of the pre-test results of the database course, as shown in table 1. The
statistical comparison table of the comprehensive situation of the post-test results of the database
course, as shown in table 2.
Table 1. The statistical comparison table of the comprehensive situation of the pre-test results of the
database course.
Class Name
Student
ID
Pass
Number
Highest
Score
Lowest Score Average Score
Standard
Deviation
Experiment Class 45 1 70 0 12 15
Reference Class 48 0 40 0 14 11.9
Table 2. The statistical comparison table of the comprehensive situation of the post-test results of the
database course.
Class Name
Student
ID
Pass
Number
Highest Score Lowest Score
Average
Score
Standard
Deviation
Experiment Class 45 42 100 0 81 19
Reference Class 48 41 92 10 69 16.5
The score comparison table clearly shows that the average score of the project-based learning
experimental group surpasses that of the reference group. However, the experimental group has a
higher standard deviation, indicating more dispersed scores within this group. Subsequently, we
will delve into which factors genuinely enhance the learning outcomes in project-based learning.
The effect size represents the difference between pre-test and post-test scores. Determining the
effect size aids in discerning which instructional and learning factors exert influence.
The formula for calculating the overall effect size is as follows:
Overall Effect Size = (Post-test Mean Score - Pre-test Mean Score) / Distribution (Standard
Deviation) Mean
The effect size for each student is the individual effect size. Each student is assumed to contribute
equally to the overall variance. The formula for calculating the individual effect size is as
follows:

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Individual Effect Size = (Individual Post-test Score - Individual Pre-test Score)/Distribution
(Standard Deviation) Mean
The larger the value of the effect size, the greater the progress of the students [7]
（John Hattie et
al.，2015）.
Total effect size and Individual effect size of the experimental class, as shown in table 3.
Table 3. Experimental class effect scale.
Student ID Pre-test Scores Post-Test Scores Individual Effect Size
2035 70 91 1.21
2201 0 93 5.35
2202 0 91 5.23
2203 25 91 3.80
2204 0 88 5.06
2205 25 94 3.94
2206 0 91 5.23
2207 45 97 3.01
2208 21 91 4.01
2209 20 87 3.85
2210 0 91 5.22
2211 20 91 4.08
2212 25 95 4.01
2213 0 89 5.12
2214 0 96 5.50
2215 15 92 4.41
2217 0 95 5.46
2218 5 78 4.22
2219 15 84 3.95
2220 10 87 4.41
2221 45 87 2.42
2222 0 62 3.55
2223 0 0 0.00
2224 20 57 2.11
2225 0 68 3.89
2226 0 10 0.58
2227 0 73 4.22
2228 0 64 3.70
2229 0 95 5.46
2230 0 89 5.12
2231 20 89 3.97
2232 0 85 4.89
2233 25 84 3.41
2234 25 66 2.34

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2235 25 78 3.07
2236 0 66 3.81
2237 25 78 3.04
2238 0 84 4.85
2239 0 88 5.06
2240 0 84 4.83
2241 16 76 3.43
2242 20 100 4.60
2243 20 71 2.92
2244 0 95 5.46
2245 0 93 5.35
Lowest Score 0 0
Highest Score 70 100
Passing Number 1 42
A Number 0 18
Average Score 12 81
Standard
Deviation
15 19
Average 17
Total effect size 3.98
Effect size data cannot be directly linked to specific teaching and learning factors. Our next step
is to identify the factors in the data that contribute to the enhancement of the effect size. Notably,
24 students have an individual effect size exceeding 4, representing 53%. Conversely, 7 students
have an individual effect size below 3, which is 16% of the total. Within this group, student No.
2035, who is retaking the course, has an individual effect size of 1.21, The subsequent data
analysis will exclude the data of this student. The individual effect sizes for students No. 2223
and No. 2226 stand at 0 and 0.58, respectively. A pertinent question arises: why do certain
students advance while others do not? The answer might lie in the variances within their
academic records. Table 4 details the specific data on learning for the experimental group that
utilized supportive learning resources.
Table 4. Learning data of supporting learning resources.

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Student ID
Tasks
Completed
Video Watch
Time
# of online
discussion
s
# of
chapter
studies
# of
face-to-
face
discussi
ons
# of
discussi
ons
Chapter
Repetition
(chapter
learning
times/chapt
er number
77)
2035 66/67 620.1min 17 105 9 1 1.36
2201 39/67 215.4min 17 118 6 1.53
2202 45/67 429.5min 17 137 6 1.78
2203 41/67 376.1min 17 65 6 1 0.84
2204 44/67 304.3min 16 96 4 1.25
2205 50/67 520.0min 15 63 5 0.82
2206 47/67 488.3min 17 204 5 2.65
2207 47/67 537.7min 13 93 6 1.21
2208 50/67 544.2min 17 90 5 1.17
2209 39/67 222.8min 14 78 5 1.01
2210 64/67 271.7min 17 135 3 1.75
2211 48/67 437.1min 16 139 8 1.81
2212 49/67 455.2min 16 88 5 1.14
2213 35/67 251.3min 13 161 5 2.09
2214 53/67 639.9min 16 194 3 2.52
2215 46/67 395.1min 8 90 7 1.17
2217 45/67 422.1min 17 161 6 2.09
2218 38/67 327.4min 12 79 5 1.03
2219 35/67 328.6min 15 128 5 1.66
2220 47/67 724.0min 14 173 4 2.25
2221 62/67 492.4min 12 74 10 0.96
2222 66/67 521.5min 17 130 6 1.69
2223 64/67 614.6min 12 86 6 1.12
2224 66/67 358.2min 1 72 2 0.94
2225 64/67 547.9min 16 92 5 1.19
2226 17/67 168.8min 0 22 3 0.29
2227 40/67 466.2min 14 136 4 1.77
2228 66/67 783.1min 15 156 4 2 2.03
2229 40/67 327.2min 0 41 1 0.53
2230 66/67 647.4min 17 108 4 1.40
2231 28/67 159.9min 15 44 4 0.57
2232 52/67 571.3min 17 195 5 2.53
2233 18/67 225.8min 13 71 4 0.92
2234 23/67 144.8min 0 37 2 0.48
2235 64/67 383.1min 5 103 2 1.34
2236 24/67 118.0min 1 64 3 0.83
2237 49/67 382.9min 15 134 4 1.74
2238 24/67 197.1min 16 65 4 0.84
2239 41/67 402.4min 12 134 4 1.74
2240 64/67 374.8min 16 102 6 1.32
2241 33/67 182.2min 14 67 3 0.87
2242 49/67 453.0min 17 140 8 2 1.82
2243 48/67 235.8min 12 88 2 1.14
2244 39/67 312.3min 13 78 2 1.01
2245 28/67 196.9min 11 71 11 0.92
Average 45.39 389.9min 12.9 104.6 4.72 1.36
From Table 4, we discern the following patterns in student learning data:

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Figure 2. Line chart of video viewing time and individual effect size
For students with a task completion rate below average yet an individual effect size surpassing
the overall effect size, the data (student ID, number of tasks completed, individual effect size) is
as follows: (2204, 44, 5.23), (2213, 35, 5.12), (2229, 40, 5.46), (2245, 28, 5.35). On the other
hand, for students with a task completion rate above average but an individual effect size falling
below the overall effect size, the data (student ID, number of tasks completed, individual effect
size) is: (2205, 50, 3.80), (2221, 62, 2.42), (2222, 66, 3.55), (2223, 64, 0), (2224, 66, 2.11).
Interestingly, 78% of students fall into two categories: those who completed more tasks than
average and had a high individual effect size, and those who completed fewer tasks than average
but had an individual effect size that was less than the overall effect size.
From Figure 2, it is evident that there is a weak correlation between the length of video watching
and the individual effect size. A notable number of students, even those who watched the video
less than the average duration, still attained a significant individual effect. This observation
underscores the importance of examining factors beyond video duration, such as chapter learning
times.
From Figure 3, we observe that 91% of students exhibit a strong correlation between the number
of chapter studies and their individual effect size. However, four students present an anomaly:
they studied fewer chapters but still achieved a significant individual effect. The learning data for
these students, represented as (student ID, number of chapter studies, individual effect sizes), is
as follows: (2229, 41, 5.46), (2238, 64, 4.85), (2244, 88, 5.46), (2245, 71, 5.35).

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Figure 3. Line chart of chapter learning times and individual effect size
(1) Self-learning attitude and independence
Firstly, examining the use of supportive learning resources, we turn our attention to the overall
learning time. Students with an individual effect size surpassing 4 have an average online
learning duration of 410.59 minutes, which exceeds the overall average of 389.96 minutes.
However, there is an exception with student 2201, whose online learning duration is just 215.4
minutes—below the average. Yet, they achieved an impressive individual effect size of 5.35.
Investigations revealed that this student dedicated more time to offline studies. While it is
challenging to measure non-online learning durations, surveys offer some insights. It became
evident that students with an effect size above 4 generally familiarized themselves with the
relevant database knowledge offline, even before their online sessions began. This group also
tended to spend more time reading offline textbooks.
Students' active participation in teacher-initiated discussions is another area of interest. These
interactions can take two forms. The first is a face-to-face setting where the teacher poses
questions and designates students to answer. The second is an online setup where the discussion
is teacher-initiated but lacks direct teacher oversight. This gives students ample time for
reflection. Notably, among the six students with an individual effect size below 3, half of them
seldom engaged in discussions. For instance, student No. 2229 only participated once but still
achieved a notable effect size of 5.46. The findings suggest that since discussions were teacher-
driven and students were somewhat passive participants, there was not a strong correlation with
genuine learning enthusiasm.

57
Additionally, there were four students who took the initiative to begin discussions. However,
their individual outcomes were not notably high. Their primary concerns revolved around basic
operational queries, seeking assistance when foundational knowledge was elusive or when they
encountered challenges during experiments.
(2) Means to seek help when encountering difficulties
Around 20% of students seek assistance from their teachers, while approximately 50% review the
provided supportive learning resources. A mere 2% turn to online inquiries for assistance, and
about half delve into textbooks for clarification.
The following list details students with an individual effect size below 3, highlighting the
frequency with which they revisited supportive learning resources and their corresponding effect
sizes. The format is (student ID, repetition frequency, personal effect size): (2221, 0.96, 2.42),
(2223, 1.12, 0), (2224, 0.93, 2.11), (2226, 0.29, 0.58), (2234, 0.48, 2.34), (2243, 1.14, 2.92).
(3) Trend analysis of student learning frequencyof note, among the six students with an effect
size below 3, the average frequency of revisiting supportive learning resources is 0.82.
Conversely, for the 14 students with an individual effect size above 5, the average frequency is
1.69. This suggests that studying course content without revisiting key materials likely results in
a diminished effect size. Consequently, we can deduce that the frequency of revisiting supportive
learning resources serves as a significant indicator of students' learning enthusiasm.
(4) The state of creative thinking
Throughout the entire project-based learning experience, no student posed any notably original or
innovative questions. During a project-based experiment, a non-standard database design plan
was intentionally introduced, and only one student proactively challenged it, presenting his own
foundational design ideas.
Among students with an individual effect size exceeding 4, 100% demonstrated the ability to
contemplate and provide solutions to foundational guiding problems. However, when faced with
more challenging issues, such as improving database query performance in the event of reduced
query efficiency, only approximately 10% of the students could offer pertinent solutions.
Figure 4. The status of student No. 2226 studying chapters
From Figure 4, it is evident that student No. 2226's engagement with the supportive learning
resources was significantly below the average in the initial stages. However, there was a sudden
spike in engagement later, with a concentrated learning period lasting as long as 168.8 minutes.
Despite this, his individual effect size was a mere 0.58, indicating a pattern suggestive of
"cramming" or merely skimming through the content without genuine comprehension.

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Figure 5. Screenshot of the status of student No. 2211 studying chapters
The online learning time of students with a personal effect size greater than 4 is 100% evenly
distributed. Figures 5 to 7 show the learning situation of students with this characteristic. The
individual effect sizes are 4.08, 4.41, and 4.60, respectively.
(5) Quality of database results
In database design, when there are three or fewer entities in a real-world scenario, 80% of
databases meet the criteria of the 3NF standard. However, as the number of entities rises, the
quality of students' database designs tends to decline. This suggests that while students generally
grasp database design and management in simple contexts through project-based learning, they
require more hands-on experience when it comes to designing and managing databases in more
complex situations.
7. SUMMARY OF LEARNING EFFECTS
(1)Strong positive correlation between high-quality project-based learning and student
enthusiasm
The "repeat degree of revisiting supportive learning resources" serves as a metric to gauge
students' enthusiasm for learning. For those with diminished motivation, this "repeat degree"
tends to be on the lower side. Conversely, students who are more invested in project-based

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learning typically exhibit a higher "repeat degree of revisiting supportive learning resources".
Thus, fostering greater interest and enthusiasm in students is pivotal for enhancing their
propensity for self-directed learning and independent study.
(2) The influence of guiding issues on learning outcomes
High-quality guiding issues can ignite students' curiosity and thirst for knowledge. The
anticipation of results further piques students' interest, prompting them to deeply ponder over the
teacher's guiding issues. By encouraging students to venture and explore, teachers can promptly
address emerging issues, broadening the scope of understanding.
(3) Does project-based learning outperform traditional remedial instruction in enhancing
problem-solving skills?
Owing to students' immersive involvement in project-based learning and the opportunities it
provides for independent planning and decision-making, there is a marked improvement in
students' initiative to learn. This continuous hands-on experience not only enhances their future
planning and decision-making capabilities but also bolsters their collaborative and
communicative skills.
Database design encompasses data management and analytical capabilities across diverse
practical application scenarios. Through project-based learning, the adeptness to migrate data
management and analysis in straightforward scenarios has been substantially augmented.
(4) Efficacy of project-based learning across performance spectrums
Groups fueled by interest and enthusiasm for learning tend to be more effective, in contrast to
those with low motivation that show little improvement in learning outcomes. This distinction
also sheds light on the broader range of individual effect sizes observed in the experimental class.
Given the multitude of learning approaches, the individual effect size emerges as an aggregate
reflection of these methods. Enthusiasm and interest in learning stand out as pivotal elements in
bolstering students' autonomous learning, self-instruction, and enhancing the effect size. With
genuine enthusiasm, project-based learning serves as a potent practical experience, enhancing
problem-solving skills and further nurturing a passion for learning. However, for those students
deficient in motivation, the impact of this approach falls short when compared to traditional
teaching methods.
(5) Classroom dialogue: a facet, not the sole catalyst, of enhanced learning
While classroom dialogue is valuable, it is not the sole method to enhance the learning
experience. When students probe and present queries, timely teacher feedback becomes pivotal in
fostering their learning journey. As highlighted in Visible Learning, John Hattie (2015)
underscores that apt feedback can substantially augment the learning process. When employed
judiciously, it facilitates students in comprehension, engagement, and crafting efficacious
strategies for information assimilation.
Consequently, as students introspect and deliberate on their unique journeys and insights
throughout the project, high-caliber bilateral discussions with educators can catalyze cognitive
growth. Such dialogues serve multiple purposes: guidance, assessment, and rectification, all
pivotal in bolstering learning.

60
The intricate design of database project-based learning hinges upon a sequenced task chain
characterized by its hierarchy and progression. This spans from initiating queries, leveraging
open-ended learning assets, to meticulous multi-faceted evaluation processes. This nuanced,
albeit often subtle, pedagogical orchestration by teachers invigorates students. It not only
endorses their self-reliance in the educator's role but also spurs them to actively partake in the
creation, articulation, and innovation of knowledge, uncovering avenues and essence of self-
guided exploration.
Project-based learning is effective in promoting active learning and interdisciplinary STEAM
education. Engaging students with project-based learning from primary and secondary schools
helps to attract students' interest in interdisciplinary knowledge early and make learning
enjoyable. Building interdisciplinary supportive learning resources is encouraged.
Teaching and learning offer no easy shortcuts; they demand an unrelenting cyclical refinement.
Given today's information deluge and the swift strides in artificial intelligence, foundational
knowledge is readily accessible online. Platforms like ChatGPT have further simplified this
knowledge acquisition process. The imperative now shifts towards nurturing students who can
navigate beyond basic knowledge points to embrace multidimensional inventive learning.
Education transcends mere doctrinarians—it is about kindling the spark of curiosity and instilling
a reverence for learning. Once this passion is aflame, it naturally kindles the intrinsic drive to
learn, paving the way for boundless ingenuity.
DECLARATION OF CONFLICTING INTERESTS
The author declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publication of this
article.
REFERENCES
[1] Translated by Xia Hui Xian, etc. [US] Sally Berman. (2004)“Multiple Intelligences and Project
Learning: Activity Design Guidance”, China Light Industry Press.
[2] Zhan Ying, (2022)“Project-based Learning Design in Database Teaching in Higher Vocational
Colleges”, Computer Knowledge and Technology, pp63-67.
[3] Xia Xue Mei, (2018)“Project-Based Learning Design: International and Local Implementation from
the Perspective of Learning Literacy”, Educational Science Press, pp31.
[4] Zhang Feng, Guan Guang Hai, (2022)“Transforming Schools: Science and Technology Innovation
Education and Project-Based Learning”, Zhejiang Education Press, pp17.
[5] Xia Xue Mei,(2020)“Implementation of project-based learning: China's construction from the
perspective of learning literacy”, Educational Science Press,pp31.
[6] Zhan Ying, Lin Su Yin, Yan Hui Jia, Guo XianHai, (2022)“Database Technology and Application-
SQL Server 2019”, Tsinghua University Press, pp300-362.
[7] John Hattie [New Zealand],( JinYing Lian, et al. Translated. ),(2015)“VISIBILE LEARING FOR
TEACHERS: MAXIMIZING IMPACT ON LEARNING”, Educational Science Press,(pp.282—
285)
AUTHOR
Zhan Ying (1970.12), female, Taizhou, Zhejiang, professor, master, research direction is database
technology, big data analysis,curriculumand instruction.

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