Pedagogical Strategies and Critical Success Factors for Enhancing Active Learning of Undergraduate Construction and Surveying Students

Abstract

Active learning is essential for students in the construction and surveying disciplines due to the practical work nature and technical demands of these professions. This research study aims to identify and evaluate effective pedagogical strategies and critical success factors (CSFs) that can enhance active learning among undergraduate construction and surveying students. Customized e-learning materials based on the Technology, Pedagogy, Content, and Knowledge (TPACK) framework were adopted to improve the learning outcomes and effectiveness for semester-long construction-related research course students. Subsequently, an institutional student-based questionnaire survey was conducted and analyzed using mean score, exploratory factor analysis, and fuzzy synthetic evaluation (FSE). Accordingly, the top five active instructional learning strategies are using examples to reinforce understanding, case studies to encourage critical thinking, videos to improve understanding, connecting course contents to current community events, and creating classroom versions of interactive games. Exploratory factor analysis classified the CSFs into five major factor categories. The FSE results indicated that the top three CSF categories enhancing students’ active learning include electronic tools as learning aids, complementary learning and feedback, and model learning system development. This study provides essential learning environments and components needed to foster active learning among undergraduate construction and surveying students in Hong Kong and worldwide.

1. Introduction

As time progresses, the mindset of human beings also evolves in different categories, significantly enhancing thinking, understanding, and cognizing ability [1]. In recent times, educational experts have been fine-tuning the education style with innovative teaching elements to enable students to learn effectively [2]. In the same way, one of the important teaching elements is the technology used for improving students’ active learning [3]. With the beginning of the 21st century (otherwise known as the information era), there has been a rise in the production and sources of information [4,5]. However, with the advent of technology, there are pedagogical strategies for enhancing active learning.

It is widely known that conventional instructional approaches, characterized by professors delivering lectures while students passively listen and take extensive notes, typically prevail in college and university classes. This traditional method of passive learning, while previously successful, may lack stimulation and engagement for students depending on the instructor’s delivery and communication skills [6]. Additionally, it may not incorporate kinesthetic activities that promote deeper thinking. Hence, it is crucial to deliberate on the essence of active learning. As explained by [7], active learning significantly enhances academic performance for college students pursuing STEM disciplines. Active learning is a broad notion that typically refers to instructional approaches that are centered around the student and aim to engage them in the learning process, the methods of which are facilitated by the teacher [8,9]. Active learning is a generally accepted teaching method that is commonly used in the global conversation about lifelong learning. Educators across all levels of education are increasingly responsible for involving students in active learning [10]. The active learning methodology has emerged as a favored approach to transform the old teacher-centered classroom into a more modern student-centered approach to learning.

Various stakeholders, including families, teachers, administrators, academics, and policy makers, are constantly seeking methods to enhance student learning. To achieve this objective, they employ many strategies such as raising standards, creating innovative curriculum, and questioning existing techniques and pedagogies, to name just a few [11]. The capability to access, evaluate, and harness information is a prerequisite for lifelong learning and the primary ingredient for the information society. In the university setting, for instance, students need information irrespective of their discipline for effective performance [12]. As explained in [13], obstacles to improving the active learning environment include teacher educators who lack competence, classrooms that are overcrowded, limited time and time constraints in studies, insufficient resources such as materials, equipment, and funds, student teachers’ lack of engagement, and the examination system. According to the findings of [14], the requirement for additional time for planning, absence of agreement among students regarding past activities, and augmentation in cognitive load for students are the main concerns of the lecturers who are involved in the teaching process. Therefore, it can be seen that there can be various challenges which arise in implementing or enhancing active learning. Hence, identification and evaluation of the pedagogical strategies and success factors to enhance active learning are very imperative.

Accordingly, the aim of this study is to identify and evaluate a series of effective pedagogical strategies and critical success factors for enhancing active learning among undergraduate construction and surveying students in Hong Kong. This study has provided a blueprint of the learning conditions or instruments and critical success factors (CSFs) that should be put together for enhanced active learning among undergraduate construction and surveying students, which has added to the existing body of knowledge and will also help better academic and information sharing in universities in both Hong Kong and overseas. The CSFs idea comprises a powerful teaching and learning management support tool that can assist in revealing some essential good practices in which satisfactory results would mean successful active learning among construction and surveying students. The findings could be used as a sound basis for enhancing the active learning of undergraduate students in other countries as well.

2. Literature Review

2.1. Active Learning in Higher Education

Active learning has emerged as a crucial pedagogical concept in higher education, especially in technical and applied disciplines such as construction and surveying [15]. Unlike traditional lecture-based teaching methods, active learning directly involves students in the educational process, encouraging participation, discussion, and hands-on engagement with the material. Research consistently demonstrates that active learning strategies significantly enhance students’ understanding, retention, and application of knowledge [16].

However, achieving active learning is not straightforward. It is fundamentally rooted in the theories of constructivist learning, which view the learning process as building new knowledge based on prior understanding [8]. Constructivism has emerged as a dominant paradigm, emphasizing that knowledge construction occurs through active engagement rather than passive reception.

Active learning is often associated with structured educational activities within a classroom setting, contrasting with passive learning methods like lectures, where knowledge is more passively acquired [17]. This distinction can be challenging when viewed through constructivist learning theories, which posit that all knowledge is constructed regardless of its origin [16]. Thus, active learning is not merely an independent student activity but a structured and supervised instructional approach that facilitates learning.

Moreover, some researchers interpret active learning as a broader learning approach, focusing on the learning process rather than solely the instructional process [18]. This broader perspective encompasses various forms of engagement, such as physical activity, interaction, social collaboration, in-depth processing, elaboration, exploration of the subject matter, and metacognitive monitoring [19]. Consequently, active learning as an instructional approach includes diverse activities that incorporate different methods and link to various cognitive processes.

2.2. Five Pedagogical Approaches (2C-2I-1R)

The five major pedagogical approaches are explained below.

i.

Constructivist: This element pertains to students’ learning inclinations and a practical learning method [20]. Rather than to react passively, students are expected to learn actively, like developing their mindsets from their own experiences and off-class activities.

ii.

Collaborative: This is meant as an approach to offering chances to students to cooperate with others [21], to train their problem-solving and viewpoints-integrating abilities, and raise their lesson engagement.

iii.

Integrative: This teaches students how to apply previous experiences and knowledge into different scenarios. To let students understand complicated theoretical concepts, this approach can offer a better learning experience, like linking ideas with real-life cases to train their problem-solving ability [22].

iv.

Reflective: This approach is related to self-reflection and evaluation; students should have the self-assessing skill by digesting opinions and advice.

v.

Inquiry-Based Learning: This element relates to students’ learning patterns [23]. Instead of fixed-answer questions, students should learn new concepts through inspirable activities, like real-life experiments and open discussions.

Pedagogical concepts are considered student-based concepts, focusing mainly on students’ learning ability by creating a basic framework for learning objectives [24]. On the other hand, approaches are more teacher-based and used as soft strategic tools in directing teachers to improve learning methods. Moreover, since details were theoretically stated, they provide adequate room for educators to discuss and brainstorm innovative educational tools [25].

2.3. Critical Success Factors (CSFs) for Enhancing Active Learning among Construction and Surveying Students—A Concise Review

Integrating software into classroom teaching is one of the critical factors that could enhance active learning among students in any discipline. Lately, university lecturers have used the software in classes and during the lesson to assist in teaching, which could help supplement lessons and students’ learning from various perspectives. For example, a student can anonymously participate in an online quiz, which could encourage timid students to learn new knowledge. Also, it was argued by [4] that student acceptance of new approaches to teaching is essential in promoting active learning. Accordingly, it was submitted by [5] that the use of technological products for learning with incentives such as videos or other searches during lessons could help a student improve their active learning and promote motivation compared to the traditional approaches.

Similarly, students may be comfortable using the internet, but that alone does not translate to active learning. Many still struggle with college-based research regarding knowing where to begin their research, narrowing broad research topics via search, and determining useful internet sources. The universities aggravate these problems as they believe that students will develop active learning independently without putting any parameters for success in place. As a result, [26] posited that adopting more interactive teaching methods and university technical support are credible factors that will enhance active learning. Moreover, [27] considered the adoption of online games as one of the best ways to improve active learning among students. Ref. [28] also submitted that games are one of the best ways to enhance active learning among students.

From the literature presented above, it is evident that existing studies have not holistically investigated and prioritized CSFs for enhancing active learning among university students. Thus, this study draws on the established evidence to explore and develop credible CSFs for improving active learning among university students.

3. Methodology for the Study

The research question addressed in this study is “what are the effective pedagogical strategies and critical success factors for enhancing active learning among undergraduate construction and surveying students in Hong Kong and how they affect?”. A quantitative research approach is adopted in achieving the research aim, which is to identify and evaluate a series of effective pedagogical strategies and critical success factors for enhancing active learning among construction and surveying students in Hong Kong. The following sections explain the data collection and analysis tools utilized in this study.

3.1. Questionnaire Survey

The College of Professional and Continuing Education (CPCE) of The Hong Kong Polytechnic University (PolyU) has always valued the essentialness of research knowledge. Therefore, a study, “Enriching Learning Experience of Construction and Surveying Students with Capstone Projects”, examined online resources’ usage and determined whether e-materials can positively impact students’ understanding of research and dissertations. In addition, the study evaluated the CSFs for enhancing active learning among university students. This research is believed to be referenceable in improving teaching methods and facilitating students’ active learning. To analyze students’ opinions, primary data were collected by questionnaire survey. Questions were drafted using the Technology, Pedagogy, Content, and Knowledge (TPACK) framework. They were then distributed to students studying construction- and surveying-related programs at PolyU School of Professional Education and Executive Development (SPEED), taking the research-based subject of SEHS3279 (Analytical Skills and Methods). Answer types were set using a 5-point Likert-type of response (1 = strongly disagree; 2 = disagree; 3 = no comment; 4 = agree; and 5 = strongly agree). A total of 158 valid survey forms were received and summarized in

Table 1. Background information of the survey respondents.

Item	Attributes	Percentage of Frequency
Gender	Male	34.18
	Female	65.82
Program of study	Building Engineering and Management (BEM) Program	27.22
	Surveying (SUV) Program	72.78
Mode of study	Full-time mode	60.76
	Part-time mode	39.24
Academic qualification	Sub-degree	62.03
	Bachelor’s degree	36.08
	Master’s degree	0.63
	No response	1.37
Working Experiences (including Internships)	No Working Experience	32.91
	<6 months	18.99
	6–12 months	6.96
	1–3 years	22.15
	4–6 years	6.33
	>6 years	12.66

for performing further statistical analysis.

3.2. Data Analysis

3.2.1. Cronbach’s Alpha Reliability Test

When evaluating the dependability of a scale, the Cronbach’s Alpha (CA) reliability test is commonly utilized. It determines the reliability of a questionnaire by measuring the internal consistency of a set of items [29]. According to [30], if the CA result ranges between 0.7 and 0.8, then readings are considered acceptable, and those beyond 0.8 are considered excellent. In this survey, the CA value for the effectiveness of the active learning instructional strategies was 0.904, while the CA for the CSFs for enhancing effective learning for research-based subjects was 0.941. This indicates that the constructs of the survey instrument have a very high degree of internal consistency.

3.2.2. Mean Score Analysis

This is a quantitative analytic approach that is used to prioritize the level of relevance of variables. The mean score analysis was used in this study to determine the relative priority ascribed to the effectiveness of the active learning instructional strategies and the CSFs for adequate active learning among construction and surveying students.

3.2.3. Exploratory Factor Analysis

Exploratory Factor Analysis (EFA) was mostly used in this study to offer objective and smaller linked categories of obstacles for rank agreement analysis. Before undertaking EFA, the suitability of the data was determined by the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. The KMO value indicates the ratio of the squared correlation between variables to the squared partial correlation between variables in measuring sampling adequacy. It has a scale from 0 to 1, with 0.5 considered acceptable for EFA. Bartlett’s sphericity test examines the presence of correlation between variables. It determines if the correlation matrix is an identity matrix, or the potential barriers are unrelated and hence unsuitable for EFA. If the value for Bartlett’s test is large and significant (less than 0.05), the population’s correlation matrix is not an identity matrix; therefore, EFA is appropriate [31]. The result of the Kaiser–Meyer–Olkin (KMO) adequacy test revealed a KMO value of 0.906, which implies an “adequate” degree of common variance [32] and which is above the minimum threshold of 0.50 recommended [33]. This study also employed Bartlett’s test of sphericity (BTS) to examine the suitability of the Principal Components Analysis (PCA) for factor extraction [32] The BTS test revealed a Chi-square value of 2042.661 and a minimal significance value (p = 0.000, df = 253) which indicates that the correlation matrix is not an identity matrix [29]. Based on the above results, the research data have met the prerequisites for exploratory factor analysis to proceed with reliability.

3.2.4. Fuzzy Synthetic Evaluation (FSE) of the Critical Success Factors for Enhancing Active Learning of Undergraduate Students

FSE is part of fuzzy set theory and a subdivision of Artificial Intelligence (AI) that uses fuzzy logic to gauge human judgment’s truthfulness [33]. Fuzzy logic helps FSE transcend the limitations of binary Boolean logic (Yes/No or True/False). This approach is a suitable assessment method due to the imprecision and ambiguity in assigning levels to variables [34]. However, it was used in this study to calculate CSFs’ significance indices for enhancing information literacy because it uses a complex computational framework that integrates membership functions to alter and produce an objective evaluation of CSF assessment subjectivity, fuzziness, and imprecision. The FSE approach was adopted using five well-established phases [35].

Phase 1: FSE Index System Development

First, prepare the FSE index. The first-level index system for the five CSF components (henceforth, Principal Success Factor, PSF) was shown as K = (k1, k2, k3, k4, k5), where k1, k2, k3, k4, and k5 indicate PSF1, PSF2, PSF3, PSF4, and PSF5, respectively. The second-level CSF assessment index system is interpreted as follows: k1 = (k11, k12, k13, …, k1n); k2 = (k21, k22, k23, …, k2n); k3 = (k31, k32, k33, …, k3n); k4 = (k41, k42, k43, …, k4n); and k5 = (k51, k52, k53, …, k3n); and so on. These index systems were FSE input variables. CSFs were graded on a 5-point scale, where Y = 1, 2, 3, 4, 5. This shows grades Y1 (Not significant), Y2 (Less significant), Y3 (Quite significant), Y4 (Significant), and Y5 (Very significant).

Phase 2: Estimating the Weightings of the Critical Success Factors (CSFs) and the Principal Success Factors (PSFs)

The second phase of FSE involves determining CSFs and PSFs weighting (W). The weighting function displays the respondent’s evaluation of CSFs and PSFs. This research used Equation (1) to calculate normalized mean weights.

$${\mathrm{W}}_{\mathrm{i}}=\frac{\mu i}{{\sum }_{i=1}^{5}ui}, 0 < {\mathrm{w}}_i <1, \mathrm{where} {\sum }_{i=1}^5{w}_i=1$$

(1)

W_i = CSF or PSF weights, µ_i = CSF or PSF mean values. A weight function’s mean sum must equal one, represented in Equation (2).

W_i = (w₁, w₂, w₃, w₄, w₅, …, w_n)

(2)

n = CSFs number in a PSF.

Phase 3: Membership Determination for the Critical Success Factors (CSFs)

Individual CSF membership functions (MFs) are determined in the third phase of FSE. The degree to which an element is a member of a fuzzy set is measured by the MF. This number is often in the 0 to 1 range. Therefore, prior to obtaining the MF for level 2, players must first get the level 1 MF. The MF of CSFs was calculated based on the responses of the survey participants, i.e., Y1 (Not significant), Y2 (Less significant), Y3 (Quite significant), Y4 (Significant), and Y5 (Very significant). Thus, the MF of the CSF (Y_in) was determined using Equation (3).

$${\mathrm{MF}}_{\mathrm{Yin}}=\frac{P_{1Y_{in}}}{Y_1}+\frac{P_{2Y_{in}}}{Y_2}+\frac{P_{3Y_{in}}}{Y_3}+\frac{P_{4Y_{in}}}{Y_4}+\frac{P_{5Y_{in}}}{Y_5}$$

(3)

where MF_Yin = MF of CSF $Y_{in}$; $P_{J{Y}_{in}}$ (j = 1, 2, 3, 4, 5) denotes the percentage score given to CSF $Y_{in}$ by the respondents; and $\frac{P_{J{Y}_{in}}}{Y_j}$ stands for the relationship between $P_{J{Y}_{in}}$ and its grade alternative based on the rating scale. Thus, the MF of the CSF was computed as follows:

$$\mathrm{MF}{\mathrm{CSF}}_{18}=\frac{0.01}{Y_1}+\frac{0.01}{Y_2}+\frac{0.30}{Y_3}+\frac{0.52}{Y_4}+\frac{0.16}{Y_5}$$

In the FSE procedure, the “+” is used as a notation, not an addition; therefore, the MF may be written as follows: MF_CSF18 = (0.01, 0.01, 0.30, 0.52, 0.16).

Phase 4: Membership Determination for Principal Success Factors (PSFs)

Phase 4 computes PSF MFs. Having obtained the MF of the CSFs, the PSFs (D_i) MF might be calculated as a product of the fuzzy matrix of the CSF MFs (R_i) and the weighting function. Equations (4) and (5) were used to calculate R_i and D_i.

$${\mathrm{R}}_{\mathrm{i}}=\left(\begin{array}{c}{MF}_{Y_{i1}}\\ {MF}_{Y_{i2}}\\ {MF}_{Y_{i3}}\\ ...\\ {MF}_{Y_{in}}\end{array}\right) = \left(\begin{array}{ccccc}{p}_{1Y_{i1}}& P_{2Y_{i1}}& P_{3Y_{i1}}& P_{4Y_{i1}}& P_{5Y_{i1}}\\ P_{1Y_{i2}}& P_{2Y_{i2}}& P_{3Y_{i2}}& P_{4Y_{i2}}& P_{5Y_{i2}}\\ P_{1Y_{i3}}& P_{2Y_{i3}}& P_{3Y_{i3}}& P_{4Y_{i3}}& P_{5Y_{i3}}\\ ...& ...& ...& ...& ...\\ P_{1Y_{in}}& P_{2Y_{in}}& P_{3Y_{in}}& P_{4Y_{in}}& P_{5Y_{in}}\end{array}\right)$$

(4)

D_i = W_i × R_i = (d_i1, d_i2, d_i3, d_i4, d_i5, d_in)

W_i = (w₁, w₂, w₃, w₄, w₅, …, w_n), hence,

$${\mathrm{D}}_{\mathrm{i}}=({\mathrm{w}}_1, {\mathrm{w}}_2, {\mathrm{w}}_3, {\mathrm{w}}_4, {\mathrm{w}}_5, \dots , {\mathrm{w}}_{\mathrm{n}}) \times \left(\begin{array}{ccccc}{p}_{1Y_{i1}}& P_{2Y_{i1}}& P_{3Y_{i1}}& P_{4Y_{i1}}& P_{5Y_{i1}}\\ P_{1Y_{i2}}& P_{2Y_{i2}}& P_{3Y_{i2}}& P_{4Y_{i2}}& P_{5Y_{i2}}\\ P_{1Y_{i3}}& P_{2Y_{i3}}& P_{3Y_{i3}}& P_{4Y_{i3}}& P_{5Y_{i3}}\\ ...& ...& ...& ...& ...\\ P_{1Y_{in}}& P_{2Y_{in}}& P_{3Y_{in}}& P_{4Y_{in}}& P_{5Y_{in}}\end{array}\right)$$

(5)

Phase 5: Evaluating Principal Success Factor (PSF) Indices

This entails calculating PSF criticality and indices. Each significance index is the product of D_i and the rating scale (Y_i). Equation (6) was adopted for calculating the PSFs significance index and total significance.

$$\mathrm{Significance} \mathrm{index}={\sum }_{i=1}^n{(D}_i x{ Y}_i)=({\mathrm{d}}_{\mathrm{i}1}, {\mathrm{d}}_{\mathrm{i}1}, {\mathrm{d}}_{\mathrm{i}2}, {\mathrm{d}}_{\mathrm{i}3}, {\mathrm{d}}_{\mathrm{i}4}, {\mathrm{d}}_{\mathrm{i}5})\times ({\mathrm{Y}}_1, {\mathrm{Y}}_2, {\mathrm{Y}}_3, {\mathrm{Y}}_4, {\mathrm{Y}}_5)$$

(6)

4. Results and Discussion

4.1. Significant Active Learning Instructional Strategies

Table 2. Effectiveness of Active Learning Instructional Strategies (ALIS) for students.

ID	Effectiveness of Active Learning Instructional Strategies (ALIS)	Mean	S.D.	Normalized Mean	Rank
EFF 11	Using examples to consolidate understanding	4.09	0.717	1.00 *	1
EFF 10	Using case studies to stimulate critical thinking	4.06	0.679	0.96 *	2
EFF 12	Using videos to enhance understanding	4.05	0.788	0.94 *	3
EFF 07	Connecting course contents to current community events	4.00	0.700	0.87 *	4
EFF 05	Creating classroom versions of interactive games (e.g., Kahoot)	4.00	0.880	0.87 *	5
EFF 17	Providing literature to enrich understanding (e.g., Databases)	3.90	0.743	0.73 *	6
EFF 09	Assessing sample work (e.g., Resources Section)	3.87	0.711	0.69 *	7
EFF 01	Creating field trips	3.79	0.885	0.57 *	8
EFF 06	Conducting in-class polling activities	3.79	0.809	0.57 *	9
EFF 15	Introducing brainstorming questions in lectures	3.71	0.768	0.46	10
EFF 08	Using self-assessment questions (e.g., Dissertation Clinic)	3.69	0.759	0.43	11
EFF 16	Using mobile phones to assist learning	3.68	0.928	0.41	12
EFF 04	Integrating website use into course assignments	3.65	0.814	0.37	13
EFF 13	Having student presentations	3.56	0.841	0.24	14
EFF 03	Conducting in-class group discussion	3.54	0.834	0.21	15
EFF 02	Having students do in-class role-plays	3.41	0.951	0.03	16
EFF 14	Interacting with peers via e-platform (e.g., Discussion Forum)	3.39	1.000	0.00	17

S.D. is for standard deviation.

shows the summary of the seventeen active learning instructional strategies. The normalized mean values indicated that nine active learning instructional strategies are very effective and significant, with a normalized value above 0.5 benchmarks. In view of the mean and the normalized scores, the top five active learning instructional strategies based on their level of effectiveness are EFF11 (using examples to consolidate understanding), EFF10 (using case studies to stimulate critical thinking), EFF12 (using videos to enhance understanding), EFF7 (connecting course contents to current community events), and EFF5 (creating classroom versions of interactive games (e.g., Kahoot)).

Using examples to consolidate understanding (EFF11) obtained a mean score of 4.09, ranked first as the most effective active learning instructional strategy. This implies that a learning strategy without using examples is ineffective in ensuring better understanding. According to [36], using models is one of the best learning strategies in modern days. Using case studies to stimulate critical thinking (EFF10) obtained a mean score of 4.06, which ranked second. Relating situations in class to a particular case study, context-based or based on a specific development, is a vital active learning instructional strategy [37]. Ranked third with a mean value of 4.05 is using videos to enhance understanding, implying that using video is a very effective strategy for learning. Thus, ref. [38] posited that students understand complex issues faster when video is used to explain. Ranked fourth and fifth are connecting course contents to current community events (EFF7) and creating classroom versions of interactive games (EFF5), each having a mean value of 4.00. There should be a proper synergy between course contents and recent developments to keep students updated [39]. This approach requires constant review of lecture contents based on recent developments. Also, an effective learning strategy should employ active interactions among students using games [40]. This strategy is quite active as students would be more engaged and fully participate, enhancing teamwork [41].

4.2. Significant Critical Success Factors (CSFs) for Enhancing Active Learning among Undergraduate Construction and Surveying Students

Table 3. Ranking of the CSFs for enhancing active learning of undergraduate construction and surveying students.

ID	Contributing Factors	Shapiro–Wilk (p-Value)	Mean	S.D.	Normalized Mean	Rank
CSF 20	Providing opportunities to students to stay tuned with various construction issues	0.00 *	3.99	0.672	1.00 *	1
CSF 19	Uploading more subject-related online resources for students	0.00 *	3.98	0.760	0.97 *	2
CSF 11	Updating of teaching materials	0.00 *	3.94	0.782	0.87 *	3
CSF 10	Updating of teaching contents	0.00 *	3.93	0.769	0.84 *	4
CSF 23	Adopting online games to check students’ understanding	0.00 *	3.91	0.796	0.79 *	5
CSF 17	Regular updating of databases online	0.00 *	3.90	0.677	0.76 *	6
CSF 16	Using electronic appliances as a helping tool during discussion time	0.00 *	3.90	0.714	0.76 *	7
CSF 03	More interactive teaching methods should be adopted	0.00 *	3.88	0.666	0.71 *	8
CSF 06	Using e-learning tools to support the delivery of the subject contents	0.00 *	3.87	0.723	0.68 *	9
CSF 09	Interacting via the online learning system	0.00 *	3.84	0.755	0.61 *	10
CSF 07	Creating interactive assessments	0.00 *	3.84	0.797	0.61 *	11
CSF 18	Offering more technical supports from the school for online learning	0.00 *	3.83	0.722	0.58 *	12
CSF 22	Increasing the use of Moodle polling system during lecture time	0.00 *	3.81	0.836	0.53 *	13
CSF 15	Providing communication channels to assist students during their studies	0.00 *	3.79	0.787	0.47	14
CSF 21	Making use of audio materials during lecture time	0.00 *	3.79	0.837	0.47	15
CSF 05	Providing opportunities to students to participate in group discussions	0.00 *	3.75	0.773	0.37	16
CSF 04	Encouraging students to participate in the discussion forum out of lecture	0.00 *	3.74	0.898	0.34	17
CSF 02	Demonstration of research findings during lessons	0.00 *	3.73	0.694	0.32	18
CSF 01	Designing new active learning curriculum	0.00 *	3.72	0.724	0.29	19
CSF 13	Offering some lecture time for students to discuss with their peers	0.00 *	3.72	0.861	0.29	20
CSF 08	Encouraging feedback from students using online questionnaires	0.00 *	3.71	0.734	0.26	21
CSF 12	Offering some lecture time for students to conduct self-study on internet	0.00 *	3.67	0.879	0.16	22
CSF 14	Creating break-out rooms for students to discuss in groups online	0.00 *	3.61	0.894	0.00	23

S.D. is for standard deviation.

summarizes the mean value, standard deviation, and the Shapiro–Wilk test of the 23 CSFs for enhancing active learning. The Shapiro–Wilk test was significant at a 0.05 level of significance for the entire CSFs, implying that collected data are normally distributed. The normalized mean values showed thirteen CSFs are substantial for enhancing active learning, with their normalized value above the 0.5 benchmarks. With regards to the mean and the normalized values, the topmost five significant CSFs for enhancing active learning include CSF20 (providing opportunities to students to stay tuned with various construction issues), CSF19 (uploading more subject-related online resources for students), CSF11 (updating of teaching materials), CSF10 (updating of teaching contents), and CSF23 (adopting online games to assess students’ understanding).

Obtaining the topmost mean value of 3.99 is providing students with opportunities to stay tuned to various construction-related issues, which is the most significant CSF for enhancing active learning among construction and surveying students. Ref. [5] asserted that various stages of construction activities should be made available to students on the site and possible problems in each activity should be identified onsite. This should be carried out regularly on different project categories to compare learning. Thus, the classroom knowledge would be blended with practical knowledge, promoting the construction students’ active learning and enhanced understanding.

Uploading of more subject-related online resources for students is the second most significant CSF for enhancing active learning among construction and surveying students with a mean value of 3.98. Ref. [42] identified this factor as crucial to effective university learning. Furthermore, according to [26], students often learn more by referring to available online materials after each lecture to gather more information and complement classroom knowledge. Therefore, the online resources should be easy to understand and be accessible to students without any restriction in order to support the active learning of construction and surveying students.

Updating of teaching materials and course contents are ranked third and fourth with mean values of 3.94 and 3.93, respectively. With innovations and frequent transformations in the construction industry, it is imperative to change and update instructional materials, course materials, and course contents used in training construction students. This is important to ensure classroom learning is in accordance with the recent developments in the construction industry. This will boost the students’ understanding and eventually promote better learning. According to [43], since the backbone of teaching is the materials used, updating them to recent editions based on new developments would enhance up-to-date knowledge among construction and surveying students for better acquisition of knowledge.

Adopting online games to check students’ understanding is ranked fifth with a mean value of 3.91. Ref. [27] posited that to facilitate learning, lecture materials could be designed in the form of an online game where students can participate. This approach often promotes the full engagement of the students, which often enhances their intuition regarding the subject materials. Ref. [28] added that online games adoption in the classroom is a compelling approach to promoting active learning among students.

4.3. Principal Success Factors (PSFs) for Enhancing Active Learning of Research-Based Subjects

Table 4. Factor loadings and eigenvalues of the Principal Success Factors (PSFs) for active learning of research-based subjects.

ID	Clustered Groupings	Factor Loadings
PSF 1	Model learning system development	1	2	3	4	5
CSF 18	Offering more technical support from the school for online learning	0.759	-	-	-	-
CSF 10	Updating of teaching contents	0.725	-	-	-	-
CSF 11	Updating of teaching materials	0.716	-	-	-	-
CSF 20	Providing opportunities to students to stay tuned with different construction-related issues	0.668	-	-	-	-
CSF 17	Regular updating of databases online	0.643	-	-	-	-
CSF 01	Designing a new active learning curriculum	0.595	-	-	-	-
CSF 02	Demonstration of research findings during lessons	0.567	-	-	-	-
PSF 2	Peer-assisted learning and interaction
CSF 13	Offering some lecture time for students to discuss with their peers	-	0.802	-	-	-
CSF 05	Providing opportunities for students to participate in group discussions	-	0.745	-	-	-
CSF 04	Encouraging students to participate in the discussion forum out of a lecture	-	0.712	-	-	-
CSF 03	More interactive teaching methods should be adopted	-	0.636	-	-	-
CSF 15	Providing communication channels to assist students during their studies	-	0.551	-	-	-
CSF 07	Creating interactive assessments	-	0.532	-	-	-
PSF 3	E-communication and research mechanism
CSF 12	Offering some lecture time for students to conduct self-study on the internet	-	-	0.817	-	-
CSF 14	Creating break-out rooms for students to discuss in groups online	-	-	0.721	-	-
CSF 09	Interacting via the online learning system	-	-	0.587	-	-
PSF 4	Electronic tools (e-tools) as learning aids
CSF 21	Making use of audio materials during lecture time	-	-	-	0.766	-
CSF 06	Using e-learning tools to support the delivery of the subject contents	-	-	-	0.696	-
CSF 22	Increasing the use of Moodle polling system during lecture time	-	-	-	0.613	-
CSF 16	Using electronic appliances as a helping tool during discussion time	-	-	-	0.534	-
PSF 5	Complementary learning and feedback
CSF 23	Adopting online games to check students’ understanding	-	-	-	-	0.752
CSF 19	Uploading more subject-related online resources for students	-	-	-	-	0.733
CSF 08	Encouraging feedback from students using online questionnaires	-	-	-	-	0.537
Eigenvalue		4.516	3.989	2.930	2.658	1.725
% of Variance Explained		19.635	17.342	12.737	11.559	7.499
Cumulative % of Variance Explained		19.635	36.977	49.714	61.273	68.772
KMO value	0.906
BTS	Chi-square value	2042.661
	Degree of freedom (Df)	253
	Significance level (p-value)	0.000

shows the cluster of factors contributing to active learning for research-based subjects and the extracted components using Principal Components Analysis. The twenty-three (23) factors represented in the five cluster groups all have factor loadings above 0.50. This implies their level of significance and contribution to the components. The value of the factor loading of each variable is evidence of its contribution to its underlying factor group [44]. The twenty-three (23) variables thus contribute to the various underlying clusters they belong to. The individual factors are classified into five groups, which are PSF 1 (model learning system development, PSF2 (peer-assisted learning and interaction), PSF3 (the e-communication and research mechanism), PSF 4 (electronic tools (e-tools) as learning aids), and PSF5 (complementary learning and feedback).

PSF1 (model learning system development), with an eigenvalue of 4.516, explained 19.635% of the variance in the CSFs. This group consists of seven CSFs connected to a coherent model learning system for information literacy among students. The most critical in this group is providing opportunities to students to stay tuned with different construction-related issues (CSF20), with a mean value of 3.99, connoting the significance of the practical and onsite construction-related project to active learning. This involves taking construction students onsite for a visit to understand ongoing construction activities, which would blend the classroom experience.

Other crucial CSFs within PS1 are updating the teaching materials (CSF11) and teaching contents (CSF10), with mean values of 3.94 and 3.93, respectively. According to [45], the construction industry is always susceptible to new developments; hence, regularly updating teaching materials is pivotal to catching up with this new trend and promoting students’ active learning. Similarly, the constant updating of online databases (CSF17) used by students is significant. Moreover, since the learning system cannot be effective without the school’s support, providing more technical support for online learning (CSF18) and designing a new active curriculum (CSF01) are significant factors that educational institutions should not overlook.

PSF 2 (peer-assisted learning and interaction) has an eigenvalue of 3.989 and explains 17.342% of the variance in the CSFs. It consists of six (6) components connected to interaction among students during learning to enhance active learning. The three topmost crucial CSFs within PSF2 are the adoption of more interactive teaching methods (CSF03), the creation of interactive assessment (CSF07), and the provision of channels of communication to support the student during their study (CSF15). These CSFs imply that for construction students to be more information literate and to enhance active learning, there should be room for regular discussion among the students.

PSF3 (e-communication and research mechanism) has an eigenvalue of 2.930 and explains 12.737% of the variance in the CSFs. This group consists of three components, and they include CSF12 (offering some lecture time for students to conduct self-study on the internet), CSF14 (creating break-out rooms for students to discuss in groups online), and CSF9 (interacting via the online learning system). CSF12, which offers some lecture time for students to conduct self-study online, is the most significant in this group. According to [46], online self-study by the student is a veritable approach to promoting personal learning as students gather new information independently without any support. Also, CSF14 (creating a break-out room for discussion among students) is a success factor that facilitates interaction in an online class and promotes student engagement in class activities. This eventually increases learning outcomes and active learning. Moreover, interacting via the online learning system was considered by [47] as a suitable approach to sharing knowledge and information among students.

PSF4 (electronic tools (e-tools) as learning aids) has an eigenvalue of 2.658 and explains 11.559% of the variance in the CSFs. The topmost three CSFs within the PSF4 include CSF16 (using electronic appliances as a helping tool during discussion time), CSF6 (using e-learning tools to support the delivery of the subject contents), and CSF 22 (increasing the use of Moodle polling system during lecture time). CSF16 (using electronic appliances as a helping tool during discussion time) and CSF6 (using e-learning tools to support the delivery of the subject contents) are placed first and second in this group, with mean values of 3.87 and 3.90, respectively. These CSFs indicate that facilities such as computer and projector adoption in the lecture would enhance better understanding and promote active learning. Similarly, CSF 22 (increasing the use of the Moodle polling system during lecture time) has a mean value of 3.81. Ref. [48] posited that the polling system is often used as class survey based on a typical question. This approach is quite suitable for student engagement and enhances active learning.

PSF5 (complementary learning and feedback) has an eigenvalue of 1.725 and explains 7.499% of the variance in the CSFs. This group has three CSFs, and they include CSF 23 (adopting online games to check students’ understanding), CSF19 (uploading more subject-related online resources for students), and CSF 8 (encouraging feedback from students using online questionnaires). The most crucial factor in this group is CSF19 (uploading more subject-related online resources for students), with a mean value of 3.98, which has already been discussed in Section 4.2. This is closely followed by CSF19 (uploading more subject-related online resources for students), with a mean value of 3.91, already discussed in Section 4.2. However, this finding agrees with [49], where it was submitted that students often learn faster with a complementary and feedback approach either in physical class or virtual class. Further, Ref. [50] opined that adopting innovative approaches to check students’ understanding and learning is a powerful tool for active learning.

4.4. Significance Indices of the PSFs for Promoting Active Learning among Undergraduate Construction and Surveying Students

The FSE approach was adopted to determine the significance indices of the PSFs for enhancing active learning among construction and surveying students.

Table 5. Critical success factors (CSFs) and Principal Success Factors (PSFs) weightings for enhancing active learning among construction and surveying students.

ID	Contributing Factors	Mean	Weightings
PSF 1	Model Learning System Development	27.04	0.307797
CSF 18	Offering more technical supports from the school for online learning	3.83	0.142
CSF 10	Updating of teaching contents	3.93	0.145
CSF 11	Updating of teaching materials	3.94	0.146
CSF 20	Providing opportunities to students to stay tuned with different construction-related issues	3.99	0.148
CSF 17	Regular updating of databases online	3.90	0.144
CSF 01	Designing new active learning curriculum	3.72	0.138
CSF 02	Demonstration of research findings during lessons	3.73	0.138
PSF 2	Peer-assisted Learning and Interaction	22.72	0.258623
CSF 13	Offering some lecture time for students to discuss with their peers	3.72	0.164
CSF 05	Providing opportunities to students to participate in group discussions	3.75	0.165
CSF 04	Encouraging students to participate in the discussion forum out of lecture	3.74	0.165
CSF 03	More interactive teaching methods should be adopted	3.88	0.171
CSF 15	Providing communication channels to assist students during their studies	3.79	0.167
CSF 07	Creating interactive assessments	3.84	0.169
PSF 3	E-Communication and Research Mechanism	11.12	0.126579
CSF 12	Offering some lecture time for students to conduct self-study on internet	3.67	0.330
CSF 14	Creating break-out rooms for students to discuss in groups online	3.61	0.325
CSF 09	Interacting via the online learning system	3.84	0.345
PSF 4	Electronic Tools (e-tools) as Learning Aids	15.37	0.174957
CF 21	Making use of audio materials during lecture time	3.79	0.247
CSF 06	Using e-learning tools to support the delivery of the subject contents	3.87	0.252
CSF 22	Increasing the use of Moodle polling system during lecture time	3.81	0.248
CSF 16	Using electronic appliances as a helping tool during discussion time	3.9	0.254
PSF 5	Complementary Learning and Feedback	11.60	0.132043
CSF 23	Adopting online games to check students’ understanding	3.91	0.337
CSF 19	Uploading more subject-related online resources for students	3.98	0.343
CSF 08	Encouraging feedback from students using online questionnaires	3.71	0.320

summarizes the weightings of the required CSFs and the PSFs for active learning enhancement. PSF4 achieved the highest weighting index (3.88). PSF5 and PSF1 closely followed this with weighting indices of 3.87 and 3.86, respectively. Though still significant, the two least ranked PSFs are PSF2 and PSF3, with indices of 3.79 and 3.72, respectively. The MFs of the CSFs and PSFs for enhancing active learning among construction students in Hong Kong were computed using Equations (3)–(5). The MFs of the PSFs were adopted in determining the significance indices of the PSFs using Equation (6)

SI_PSF4 (electronic tools as learning aids) = (0.00, 0.02, 0.28, 0.52, 0.18) × (1, 2, 3, 4, 5) = 3.88

SI_PSF5 (complementary learning and feedback) = (0.01, 0.03, 0.29, 0.50, 0.17) ×(1, 2, 3, 4, 5) = 3.87

SI_PSF1 (model learning system development) = (0.02, 0.04, 0.29, 0.50, 0.15) × (1, 2, 3, 4, 5) = 3.86

SI_PSF2 (peer-assisted learning and interaction) = (0.01, 0.02, 0.28, 0.51, 0.19) × (1, 2, 3, 4, 5) = 3.79

SI_PSF3 (e-communication and research mechanism) = (0.01, 0.02, 0.26, 0.51, 0.20) × (1, 2, 3, 4, 5) = 3.72

The computed significance indices of the PSFs fall within the second level of the FSE index system initialized in stage 1 of the FSE approach; hence, they are quite significant. However, the PSF weightings in Table 4 entail PSF1 (0.308), PSF2 (0.259), PSF3 (0.127), PSF4 (0.175), and PSF5 (0.132). The PSFs of MFs include SI_PSF1 = (0.00, 0.02, 0.28, 0.52, 0.18), SI_PSF2 = (0.01, 0.03, 0.29, 0.50, 0.17), SI_PSF3 = (0.02, 0.04, 0.29, 0.50, 0.15), SI_PSF4 = (0.01, 0.02, 0.28, 0.51, 0.19), and SI_PSF5 = (0.01, 0.02, 0.26, 0.51, 0.20). Regarding the weightings where MFs of the PSFs and the alternative grades are based on the assessment scale, the entire significance indices of the 23 CSFs for enhancing active learning among construction students in Hong Kong were determined using Equation (7). The process is described below:

However, W_overall = (0.308, 0.259, 0.127, 0.175, 0.132)

$$\mathrm{Also}, {\mathrm{R}}_{\mathrm{overall}}=\left(\begin{array}{ccccc}0.00& 0.02& 0.28& 0.52& 0.18\\ 0.01& 0.03& 0.29& 0.50& 0.17\\ 0.02& 0.04& 0.29& 0.50& 0.15\\ 0.01& 0.02& 0.28& 0.51& 0.19\\ 0.01& 0.02& 0.26& 0.51& 0.20\end{array}\right)$$

$${\mathrm{D}}_{\mathrm{overall}}={\mathrm{W}}_{\mathrm{overall}} \times {\mathrm{R}}_{\mathrm{overall}}=0.308, 0.259, 0.127, 0.175, 0.132 \times \left(\begin{array}{ccccc}0.00& 0.02& 0.28& 0.52& 0.18\\ 0.01& 0.03& 0.29& 0.50& 0.17\\ 0.02& 0.04& 0.29& 0.50& 0.15\\ 0.01& 0.02& 0.28& 0.51& 0.19\\ 0.01& 0.02& 0.26& 0.51& 0.20\end{array}\right)$$

D_overall = (0.008, 0.025, 0.282, 0.510, 0.178)

$$\mathrm{Recall}, \mathrm{significance} \mathrm{index}={\sum }_{i=1}^n{(D}_i x{ Y}_i)=({\mathrm{d}}_{\mathrm{i}1}, {\mathrm{d}}_{\mathrm{i}1}, {\mathrm{d}}_{\mathrm{i}2}, {\mathrm{d}}_{\mathrm{i}3}, {\mathrm{d}}_{\mathrm{i}4}, {\mathrm{d}}_{\mathrm{i}5})\times ({\mathrm{Y}}_1, {\mathrm{Y}}_2, {\mathrm{Y}}_3, {\mathrm{Y}}_4, {\mathrm{Y}}_5)$$

(7)

Hence, S_overall = (0.008, 0.025, 0.282, 0.510, 0.178) × (1, 2, 3, 4, 5)

S_overall = (0.008 × 1) + (0.025 × 2) + (0.282 × 3) + (0.510 × 4) + (0.178 × 5)

S_overall = 3.834 (significant)

Table 6. Membership function (MF) of critical success factor (CSF) and Principal Success Factor (PSF) weightings for enhancing active learning among undergraduate construction and surveying students.

ID	Contributing Factors	$\mathit{W}\mathit{i}$	MFs (Level 2)	MFs (Level 1)
PSF 1	Model Learning System Development	0.307797		(0.00, 0.02, 0.28, 0.52, 0.18)
CSF 18	Offering more technical supports from the school for online learning	0.142	(0.01, 0.01, 0.30, 0.52, 0.16)
CSF 10	Updating of teaching contents	0.145	(0.00, 0.03, 0.24, 0.50, 0.23)
CSF 11	Updating of teaching materials	0.146	(0.00, 0.03, 0.26, 0.47, 0.25)
CSF 20	Providing opportunities to students to stay tuned with different construction-related issues	0.148	(0.00, 0.00, 0.23, 0.55, 0.22)
CSF 17	Regular updating of databases online	0.144	(0.00, 0.01, 0.26. 0.55, 0.18)
CSF 01	Designing new active learning curriculum	0.138	(0.01, 0.02, 0.34, 0.51, 0.12)
CSF 02	Demonstration of research findings during lessons	0.138	(0.00, 0.03, 0.31, 0.55, 0.11)
PSF 2	Peer-assisted Learning and Interaction	0.258623		(0.01, 0.03, 0.29, 0.50, 0.17)
CSF 13	Offering some lecture time for students to discuss with their peers	0.164	(0.03, 0.02, 0.30, 0.50, 0.15)
CSF 05	Providing opportunities to students to participate in group discussions	0.165	(0.01, 0.04, 0.30, 0.51, 0.15)
CSF 04	Encouraging students to participate in the discussion forum out of lecture	0.165	(0.02, 0.05, 0.30, 0.44, 0.19)
CSF 03	More interactive teaching methods should be adopted	0.171	(0.00, 0.01, 0.27, 0.56, 0.16)
CSF 15	Providing communication channels to assist students during their studies	0.167	(0.01, 0.03, 0.28, 0.52, 0.16)
CSF 07	Creating interactive assessments	0.169	(0.01, 0.03, 0.29, 0.47, 0.20)
PSF 3	E-Communication and Research Mechanism	0.126579		(0.02, 0.04, 0.29, 0.50, 0.15)
CSF 12	Offering some lecture time for students to conduct self-study on internet	0.330	(0.03, 0.04, 0.29, 0.50, 0.14)
CSF 14	Creating break-out rooms for students to discuss in groups online	0.325	(0.03, 0.05, 0.32, 0.47, 0.13)
CSF 09	Interacting via the online learning system	0.345	(0.01, 0.03, 0.26, 0.54, 0.17)
PSF 4	Electronic Tools (e-tools) as Learning Aids	0.174957		(0.01, 0.02, 0.28, 0.51, 0.19)
CSF 21	Making use of audio materials during lecture time	0.247	(0.02, 0.03, 0.28, 0.49, 0.18)
CSF 06	Using e-learning tools to support the delivery of the subject contents	0.252	(0.01, 0.01, 0.27, 0.54, 0.18)
CSF 22	Increasing the use of Moodle polling system during lecture time	0.248	(0.02, 0.02, 0.29, 0.48, 0.19)
CSF 16	Using electronic appliances as a helping tool during discussion	0.254	(0.00, 0.01, 0.27, 0.52, 0.20)
PSF 5	Complementary Learning and Feedback	0.132043
CSF 23	Adopting online games to check students’ understanding	0.337	(0.01, 0.01, 0.25, 0.50, 0.22)	(0.01, 0.02, 0.26, 0.51, 0.20)
CSF 19	Uploading more subject-related online resources for students	0.343	(0.01, 0.01, 0.22, 0.52, 0.25)
CSF 08	Encouraging feedback from students using online questionnaires	0.320	(0.00, 0.05, 0.32, 0.52, 0.12)

presents the Membership function (MF) of critical success factor (CSF) and Principal Success Factor (PSF) weightings for enhancing active learning among undergraduate construction and surveying students.

5. Discussion, Implications, and Policy Recommendations for Enhancing Active Learning of Undergraduate Construction and Surveying Students

The top five active learning instructional strategies include the following: using examples to consolidate understanding, using case studies to stimulate critical thinking, using videos to enhance understanding, connecting course contents to current community events, and creating classroom versions of interactive games (e.g., Kahoot). These are quite effective active learning strategies that should be fully adopted to enjoy a better learning environment. According to [51], when examples are used for a study, case studies are adopted and interactions are allowed; then, such an integrated approach is definitely a practical learning approach. Similarly, both [28] and [5] opined that using games would trigger the student’s brain to develop more understanding since such a gaming approach is an active learning strategy.

Moreover, the five groups of CSFs for enhancing active learning are rated as being significant (3.834), which deserves the maximum attention of the university management. The most significant PSF is adopting electronic tools as learning aids, with the highest significance index of 3.88 based on a 5-point rating scale. Another crucial PSF, second on the list, is complementary learning and feedback promotion, with a significance index of 3.87. This is followed by the model learning system development (3.86). Finally, peer-assisted learning and interaction (3.79), and e-communication and research mechanism (3.72) occupy the fourth and fifth positions based on their significance indices. The findings are exciting and representative since active learning requires electronic system development, complementary learning, the establishment of a model learning system, and peer learning and interaction.

To promote electronic and online learning, various infrastructures to support the learning process, such as audio materials, internet resources, and complementing facilities, should be implemented [26]. This will smooth the teaching activities, promote understanding, and eventually enhance active learning [52]. Moreover, complementary learning and feedback are strategies to ensure students are carried along in teaching activities [43]. Thus, a system for online games incorporated into lectures should be promoted. In addition, relevant materials to support teaching should be made available to complement the classroom facilities. Overall, a feedback system where students lodge complaints on their literacy level regarding a subject matter should be implemented. This will help boost active learning.

A prototype learning system that is used for transferring knowledge and information is necessary, such as teaching materials, curriculum, and online databases. Developing these aspects would trigger better student active learning [4]. Likewise, the learning system should be interactive, allowing students to relate and share ideas on a given topic. This approach has been seen as a veritable means for information transfer and literacy among students in any class or degree.

Nevertheless, because of the present uncertainty brought about by the COVID-19 global pandemic and the development of innovative approaches to learning, universities should put in place efforts to ensure that students are more actively learning and the teaching strategies are effective [3]. Therefore, the findings of this study have practical implications for academic organizations. First, this study established the essential active learning instructional pedagogical strategies that should be implemented to facilitate the required CSFs that have not been documented in the construction domain. Second, this study established sets of CSFs for enhancing active learning among construction students in Hong Kong, which have not gained much attention yet are conducive to promoting the success of active learning. Third, previous research has focused on determining successful learning based on policy-connected conditions. Thus, this study captured and explored the various CSFs empirically, which could assist academic organizations in decision-making. More importantly, the results of this study unveil the importance of developing an electronic learning system and the complementary feedback approach which should be present to enhance better active learning procedures. Fourth, electronic research tools such as learning aids, complementary learning and feedback, the model learning system, peer-assisted learning and interaction, and e-communication and research are considered the principal management factors that, if appropriately put together, can lead to credible active learning enjoyed by the students. Thus, the findings on the CSFs that should be implemented would be useful to decision-makers in educational institutions when considering improvements in active learning. The findings of this study can serve as a point of reference for educators interested in enhancing tertiary education methods more interactively. This could include incorporating more active teaching–learning elements for students during lessons, mainly when instruction is provided online.

The outcome of this study has also generated some worthwhile theoretical implications. First, the 23 sets of CSFs for enhancing active learning strategies constitute the first theoretical set of conditions and pragmatic strategies that can promote active learning. Second, this study contributes to the CSFs theory in the tertiary education sector’s construction and surveying field of study. Third, the pinpointed CSFs and active learning strategies may even form a sound basis for any exploratory studies in the future which are useful for cross-comparisons from one region or country to another.

6. Conclusions

This paper has rigorously developed active learning pedagogical strategies and CSFs for enhancing the active learning of undergraduate construction and surveying students in the context of Hong Kong. The research process entailed a detailed literature review and questionnaire survey administration. Altogether, 158 valid responses were collected via the institutional student-based survey, which were collated and analyzed using mean score ranking, exploratory factor analysis, and fuzzy synthetic evaluation approaches. The mean score analysis of the active learning instructional strategies revealed that the top five effective pedagogical strategies include using examples to consolidate understanding, using case studies to stimulate critical thinking, using videos to enhance understanding, connecting course contents to current community events, and creating classroom versions of interactive games (e.g., Kahoot).

The top five most essential CSFs are providing opportunities for students to stay tuned with various construction issues, uploading more subject-related online resources for students, updating of teaching materials, updating of teaching contents, and adopting online games to assess students’ understanding. Moreover, the five clustered groups of CSFs for enhancing active learning include adopting electronic tools as learning aids, complementary learning and feedback promotion, model learning system development, peer-assisted learning and interaction, and the e-communication and research mechanism, altogether explaining about 68.772% of the variance of the CSFs for enhancing active learning.

The FSE method showed that the five Principal Success Factors were all significant, with their significance indices above 3.50 on a rating scale of 5. Thus, the findings of this study have made a vital theoretical and practical contribution to the development of active learning among undergraduate construction and surveying students. The results generated are sets of essential processes and effective strategies that must be implemented to enhance active learning among construction and surveying students. The observations from this study will be referenceable for teachers who plan on improving their teaching methods at the university level in a more interactive way as well as deciding to hold more active teaching–learning elements for students during lessons, especially during online teaching periods. Further studies should also consider viewpoints from other stakeholders besides students, such as educators and government officials. This study established the first set of CSFs to promote active learning from the student’s perspective. Despite the contributions of this research, some limitations exist; only data from the construction and surveying students were solicited and considered in this study. Future studies can consider various categories and program of study levels of students from other fields for cross-comparisons to develop a more robust list of effective pedagogical strategies and critical success factors (CSFs) for enhancing the active learning of university students from various study disciplines.