The Impact of Online Interactive Teaching on University Students’ Deep Learning—The Perspective of Self-Determination

Abstract

In the process of educational practice in the digital age, the higher education system has shifted to the online model, and the training of college students’ deep learning has become the core issue. While online teaching offers great possibilities for education, its inherent lack of interaction has always been a major limiting factor in its effectiveness. To address this challenge, interactive pedagogy is an interaction-based teaching model in which students learn knowledge more effectively through communication and interaction with teachers and classmates, which helps to improve students’ learning abilities. This study, rooted in the theoretical framework of self-determination theory and utilizing structural equation modeling for empirical analysis, seeks to investigate the influence of online interactive teaching on deep learning among university students. The research reveals that interactive teaching significantly and directly contributes to deep learning, while the fulfillment of university students’ three fundamental psychological needs indirectly fosters deep learning by fostering intrinsic motivation. This study uncovers novel dimensions of factors influencing deep learning and underscores the vital role of interactive teaching in fostering deep learning among university students. Moreover, this not only deepens the application of self-determination theory in education but also provides valuable theoretical support for online education practices.

1. Introduction

In recent years, driven by the pursuit of high-quality socio-economic development, there has been an increasing demand for greater emphasis on improving quality, effectiveness, and sustainability in education [1]. In the current education mode, characterized by the demands of the contemporary era and its evolving stages, compared to traditional formats and standardized teaching methods, greater emphasis is placed on nurturing students’ innovative spirit and practical abilities [2]. In traditional classrooms, typically characterized by teacher-centered instruction, students often lack the enthusiasm to volunteer answers, and some teachers fail to facilitate active questioning, leading to markedly limited interaction between teachers and students [3]. The traditional education model regards teachers as the primary disseminators of knowledge, with students expected to accept and master this knowledge. However, the limitation of this model lies in its failure to provide students with an environment conducive to autonomous exploration and practical opportunities. This inadequacy no longer meets the contemporary societal demand for cultivating talents with comprehensive capabilities [4]. Today necessitates a paradigm shift in educational philosophy, transitioning from a mere emphasis on knowledge transmission to a robust focus on nurturing students’ abilities, particularly their creative prowess.

Innovation ability refers to individuals’ capacity to generate novel ideas, methods, or solutions when confronted with unfamiliar problems and challenges [5]. It is a fundamental ability for college students to adapt to changes and a significant factor influential in students’ learning outcomes, helping them tackle complex problems and fostering independent thinking. However, deep learning takes it a step further by enhancing the depth and breadth of students’ thinking and their capacity to apply knowledge to practical situations. Fullan and Langworthy observed that students possess the capability for deep learning, enabling them to proactively tackle challenges, view issues from various angles, and translate ideas into practical applications [6]. Felder and Richard highlighted that students’ deficiency in deep learning ability can lead to mental rigidity, hinder the clash of ideas, thereby constraining their developmental prospects and impeding their adaptation to the rapidly evolving social landscape [7]. Therefore, educators should prioritize fostering students’ deep learning abilities in the classroom.

With the rapid development of information technology and the advancement of digital education, online interactive teaching and learning are in the developmental stage and have received extensive attention from the educational community [8]. Online interactive teaching refers to learners being able to overcome the limitations of traditional learning spaces through the use of online learning platforms, such as live and recorded classes [9,10]. Among them, live classes are a type of synchronous online teaching method utilizing the internet as the carrier, which constitutes one of the primary formats in the online classroom. They enable teacher-student interaction, student-student interaction, and simulate a real classroom environment [11]. While some researchers have pointed out that online classrooms may be more effective than traditional offline classrooms [12], the development of online teaching still faces some dilemmas [13]. Students in an online classroom primarily learn independently, are susceptible to distractions, and lack an immersive learning atmosphere. This not only results in a lack of student motivation but also impacts the effectiveness of teaching and learning [14]. The advantages of online synchronous teaching interactions are unparalleled compared to offline interactions, including breaking through time and space constraints, facilitating text exchanges, and providing recordable playback. Sarker et al. found that the questioning rate and interaction frequency of students in online teaching are significantly higher than those in offline classroom teaching due to the convenience of overcoming time and space limitations brought by educational technology, changes in students’ psychology and modes of expression, and the reliance on written language for interaction in online teaching, thus alleviating students from facing collective pressure [15]. Therefore, the use of interactive teaching methods by teachers in online teaching can be important in terms of facilitating teacher-student communication and enhancing teaching efficacy [16]. The majority of existing studies on online teaching have predominantly explored it from the standpoint of educators. Regan and Kelley’s analysis involved a study on the challenges confronted by educators in online learning environments (OLEs), primarily encompassing limitations on instructional delivery and the propensity for heightened emotional strain [17]. With learners constituting the primary cohort of online education, the focus predominantly lies on qualitative research methodologies, primarily delving into learner attributes such as motivation [18], experience [19], self-regulation, and related facets [20]. Research originating from students themselves is scant, particularly considering that self-determination theory, which probes into students’ psychological dynamics during learning, concentrates on their intrinsic motivation, positing that when individuals perceive autonomy, competence, and relatedness, they are prone to fostering lasting intrinsic motivation, thereby fostering heightened learner engagement [21]. This intrinsic drive is closely intertwined with deep learning, which underscores the comprehension, synthesis, and application of knowledge—the very essence of learning propelled by intrinsic motivation [22]. Perspectives derived from self-determination theory aid in comprehending the formation of students’ intrinsic motivation within the framework of deep learning and elucidate how such motivation can be nurtured through the fulfillment of their autonomy, competence, and relatedness needs [23].

The above research has revealed that there is a significant dearth of studies addressing deep learning among college students within the current landscape of online education. Through the lens of self-determination theory and employing empirical methods, this study aims to explore the impact of interactive teaching in online teaching on college students’ deep learning, thereby bridging the existing research lacuna. This study mainly addresses the following two issues: 1. The impact of online interactive teaching on college students’ in-depth learning. 2. The impact of self-determination theory on college students’ in-depth learning. This study is poised to offer systematic theoretical underpinnings for online pedagogy and serve as a benchmark for comparable interactive teaching (Figure 1).

2. Literature Review

2.1. Deep Learning

Deep learning is a holistic approach to learning that promotes the construction, transfer, and application of students’ knowledge systems. The approach encourages active participation and proactive exploration by students and gradually deepens their understanding of knowledge in this process [24]. Its approach plays a vital role in fostering students’ creativity and critical thinking abilities [25].

Existing research mainly categorizes factors influencing deep learning into two types: internal factors and external factors. Internal factors mainly involve students’ behaviors and psychological states during learning. Núñez and León noted that a high level of interest in the course can foster autonomy in learning, thereby enhancing the effectiveness of deep learning [22]. Gutierrez de Blume, A.P., highlighted that effective learning strategies and robust self-control assist students in comprehending and mastering knowledge, crucial for optimizing the deep learning process [26]. The above-mentioned literature examines the significance of learners’ internal factors on the effectiveness of deep learning. However, as research progresses, scholars have found that external factors, such as the learning environment and technical support, exert a notable influence on the effectiveness of deep learning. Despite low student engagement, employing interactive seminar classes or suitable teaching methodologies can further bolster students’ abilities in deep learning [27]. In the era of rapid development of information-based education, emerging educational resources, such as online teaching platforms, offer versatile teaching modalities [28], such as the teaching method of visualization of complex programming through online virtual learning environments, which not only deepen the understanding of subject knowledge but also stimulate students’ intrinsic learning motivation, thus promoting students’ deep learning ability [29]. The previous literature solely addressed the significance of external factors in enhancing deep learning ability, overlooking the supportive role of teachers’ own abilities and teaching methods in fostering learners’ deep learning capacity. Consequently, numerous researchers have investigated teachers’ behavior in classrooms, instructional techniques, and student interaction. Núñez and León employed empirical research to demonstrate that teachers significantly contribute to enhancing learners’ deep learning ability and serve as pivotal elements in fostering students’ intrinsic motivation, deep learning, and class vitality [22]. Hence, teachers require adequate guidance and support for students in the classroom. Likewise, learners themselves should actively engage in interactions with teachers, problem-solving, and collaboration with peers, all of which are vital strategies for fostering deep learning among students [30].

The literature reviewed above indicates that most of the existing research results consider the influence of internal or external factors on the improvement of deep learning ability. However, they do not fully consider the joint action of these factors, which has significant limitations. On this basis, this study aims to comprehensively analyze the importance of both internal and external factors for enhancing deep learning ability.

2.2. Online Interactive Teaching

Online interactive teaching is a novel approach to online education that fully utilizes modern tools, prioritizes learner engagement, and underscores real-time interaction and personalization. Its primary objective is to establish knowledge frameworks centered around the teacher, highlight the student’s role as the main participant, and foster intrinsic motivation for active learning [31]. Consequently, scholars have extensively investigated online interactive teaching. Sit et al. and Phipps observed that online learning environments offer students opportunities to cultivate creative thinking and problem-solving skills, emphasizing that interaction plays a pivotal role in shaping the quality of students’ online learning experiences, as reported in their respective studies [32,33]. Interactive pedagogies in online environments are classified into two main types: teacher–student interactions and student-student interactions.

In terms of teacher–student interaction, Xie et al. analyzed the fundamentals and supportive features of teacher–student interaction in online learning spaces, including teaching support, analysis, evaluation, and management services. They developed and proposed a preliminary model of teacher–student interaction, pointing out that this model can enhance students’ engagement in the interaction process. After the intervention, students’ final exam scores and innovative problem-solving skills significantly improved. However, the model was tested with a small number of students, and its feasibility and correctness remain to be proven [34]. Sun et al. integrated open courseware into the flipped classroom model and distance learning, comparing significant differences in teacher–student interactions between the two instructional modes. They indicated that the flipped classroom model positively impacts student achievement with sufficient teacher–student interactions [35]. Sun et al. explored the impact of teacher–student interaction on learning outcomes and the mediating role of psychological climate and learning rubrics in online education. They demonstrated that teacher–student interaction not only directly affects students’ learning outcomes but also influences them through the mediating roles of psychological climate and learning. However, the literature did not examine the impact of teacher–student interaction on promoting students’ deeper learning abilities [36]. Azmat and Ahmad identified a global challenge in online learning due to the absence of faculty-student interaction. They recommended counseling students in online courses and taking steps to increase faculty-student interaction [37].

In terms of student-student interaction, Krouska et al. pointed out that mobile game-based learning facilitates teaching and learning. They argue that integrating games into the educational process can increase the frequency of interaction between students, enhancing their motivation and effectiveness [38]. However, this method of interaction has a greater impact on students with strong self-control in their learning. Conversely, it is not conducive to improving the learning ability of those with poor self-control; instead, it may hinder their learning effectiveness. Ahshan proposes a framework for implementing activities that promote active participation in online learning, improve interaction between students, and enhance teaching and learning. However, the framework fails to consider the critical role of teachers in distance learning and lacks credibility [39].

Although the aforementioned literature suggests that more effective teacher–student and student-student interaction methods can contribute to enhancing students’ learning ability, there is no direct evidence to support the idea that these interaction methods impact students’ depth of learning. Additionally, there is a lack of experimental evidence to demonstrate a direct contributory effect of online interactive teaching on enhancing students’ depth of learning.

2.3. Self-Determination Theory

Self-determination theory (SDT), initially developed by Deci and Ryan in the 1970s [40], describes the internal motivational processes through which individuals make decisions regarding self-improvement, self-actualization, and intrinsic interest. When individuals interact with the external environment, they receive positive feedback and recognition, which can significantly influence them. This is because positive feedback and recognition enhance an individual’s self-confidence, thereby promoting the occurrence and maintenance of intrinsic motivation. However, maintaining intrinsic motivation also requires supportive external environmental conditions; otherwise, it may be compromised, leading to a decline in intrinsic motivation. Additionally, SDT identifies three fundamental psychological needs: autonomy, competence, and relatedness [40].

Autonomy refers to an individual’s behavior being driven by their own will and self-determination rather than being controlled by others. In an educational context, autonomy refers to the choices and psychological freedom experienced by students in their learning activities [41]. Competence, also known as competency, refers to individuals’ effective practice behavior and the development of their existing abilities. Students who perceive their own effectiveness can recognize the efficient operation of their skills and exert control over their academic tasks within their environmental interactions [41]. Relatedness refers to the individual’s need to form and maintain close and stable relationships [41].

Studies have shown that satisfying the three psychological needs of SDT greatly stimulates students’ intrinsic motivation and improves their learning ability [42]. Autonomy allows students to learn on their own terms, avoiding fixed and controlling language that imposes unnecessary psychological pressure on them [43]. Students’ self-confidence in learning is enhanced when they feel they possess the required skills and competence [44]. When students feel connected and empathetic towards others, it promotes interaction and collaboration to achieve goals [45]. The above thesis focuses on the influence of individual needs in autonomy, competence, and relatedness on the improvement of learning effects but does not jointly analyze their collective importance in enhancing students’ learning ability. The results indicate that students’ ability stands out as they experience a higher level of autonomy, competence, and relatedness [46]. The applicability of SDT in online learning environments has been demonstrated in pertinent studies [47]. Using SDT as the framework, it has been confirmed that the online teaching environment utilizes discussion interaction to influence students’ sense of relatedness. This interactive behavior can significantly enhance students’ satisfaction with their relatedness [48]. Müller employed the empirical research method to discuss fundamental psychological needs, motivation, and vitality. SDT served as the theoretical framework to analyze the psychological state and behavioral changes of college students amidst the epidemic [49].

Through the analysis of the literature above, it is evident that there is insufficient research examining the improvement of students’ deep learning abilities when all three types of SDT needs are satisfied. Additionally, there is a scarcity of studies investigating the mediating role played by students’ intrinsic motivation. Therefore, this study will be based on SDT to explore the impact of online interactive teaching on deep learning and elucidate the mechanism through which online interactive teaching influences deep learning.

3. Research Hypotheses and Theoretical Model

3.1. The Impact of Online Interactive Teaching and Learning on Deep Learning

Interactive teaching emphasizes communication between teachers and students, as well as collaborative discussions among students, to foster a sense of relatedness among students [50]. According to the concept of relatedness in SDT, relatedness denotes the sense of connection and belonging that individuals experience with others, communities, or environments. Therefore, interactive teaching cultivates a positive learning environment by affording opportunities for interaction with others and nurturing students’ sense of connection and belonging. In this study, online interactive teaching and learning are considered to fulfill the concept of “relatedness” in SDT. Cognitive learning theory posits that the depth of student learning is closely tied to classroom teaching. Teachers should integrate previously acquired knowledge and new concepts through interactive methods in class, aiding students in constructing their knowledge framework. Research findings indicate that both teacher–student interaction and student-student interaction in online classrooms significantly influence student learning outcomes [51]. Therefore, this study posits the hypotheses:

H1: 

Online interactive teaching positively impacts deep learning.

3.2. Self-Determination Theory and Deep Learning

Online teaching, with its unique advantages, is gradually transforming the modalities of education. Among these changes, the influence of online interactive teaching and learning on students’ perceptions of autonomy and intrinsic motivation has garnered significant attention [52]. SDT underscores the active role of the self in the motivation process [40]. Online interactive teaching offers educators a plethora of instructional tools and methodologies to more effectively cater to students’ learning requirements, including various teaching resources and activities like online discussion platforms, interactive game-based quizzes, “raise hand” features, etc. It can enhance students’ learning outcomes and motivation in online classes [53]. Additionally, it facilitates more active student participation by affording them greater autonomy through a wider array of independent choices. This increased autonomy fosters a greater sense of control, thereby augmenting student autonomy. Online interactive teaching prioritizes student engagement and experience, thereby stimulating their interest and curiosity in learning. When students are interested in what they are learning and gain a sense of achievement and satisfaction from the interaction, their intrinsic motivation will be enhanced [54].

When students perceive learning as voluntary, self-directed, and fulfilling their inner needs, their intrinsic motivation is enhanced [55]. This enhanced intrinsic motivation encourages learners to be more actively involved in deep learning, improving learning outcomes and efficiency. SDT emphasizes individual autonomy, encouraging learners to demonstrate initiative in the process of deep learning, thus effectively promoting deep learning abilities. When perceived competence is improved, the learners’ ability to understand and grasp the learning task is also enhanced. This enhancement means that learners are more confident in facing challenges and difficulties, believing that they can overcome obstacles and succeed [44]. This confidence encourages learners to delve deeper into acquiring knowledge and skills, thereby improving the overall learning effect. Simultaneously, the improvement in perceived competence renders the learning process more challenging and appealing, stimulating learners’ intrinsic motivation [56]. Learners will feel satisfied and happy due to their in-depth understanding and grasp of the learning task, thus devoting more effort to learning and fostering their continuous progress. Therefore, this study posits the hypotheses:

H2a: 

Online interactive teaching and learning positively affect perceived autonomy.

H2b: 

Online interactive teaching and learning positively affect intrinsic motivation.

H2c: 

Perceived competence has a positive effect on deep learning.

H2d: 

Perceived autonomy has a positive effect on deep learning.

H2e: 

Intrinsic motivation has a positive effect on deep learning.

3.3. The Mediating Role of Perceived Competence and Perceived Autonomy

In the online teaching environment, interactive teaching is crucial for the integration of teaching and learning. Interaction allows students to address their psychological needs. Especially when students have a strong sense of internal control, they are better able to recognize and address their psychological needs [57]. Therefore, students’ awareness of their psychological needs can effectively promote deep learning. When students perceive autonomy, they participate more actively in interactive classroom activities and demonstrate a greater willingness to invest time and effort in deep learning [52]. With a higher perceived competence, students can more accurately assess their learning status, identify problems and needs, and consequently, autonomously select learning strategies and methods that best suit them. This autonomy will encourage students to delve deeper into learning content, fostering a comprehensive understanding and application of knowledge [43], thereby advancing deep learning. Therefore, this study posits the hypotheses:

H3a: 

Perceived autonomy mediates the effects of online interactive teaching on deep learning.

H3b: 

Perceived competence mediates the effects of perceived autonomy on deep learning.

3.4. The Mediating Role of Intrinsic Motivation

In self-determination theory (SDT), fulfilling three psychological needs can foster intrinsic motivation in learners. Research has shown that students engage in deep and strategic learning when teachers enhance their intrinsic motivation through intellectual stimulation, including interactive teaching styles, challenging students, and fostering independent thinking [58]. When students perceive a high degree of autonomy, their deep learning behavior is self-determined, which is driven by intrinsic motivation. When engaged in optimally challenging tasks, learners become highly interested in classroom activities. This enhances intrinsic motivation, enabling them to perceive, understand, capture, process, and integrate information more effectively [59]. From this perspective, intrinsic motivation is regarded as a pivotal driver of deep learning. Therefore, this study posits the hypotheses:

H4a: 

Intrinsic motivation mediates the effects of online interactive teaching and learning on deep learning.

H4b: 

Intrinsic motivation mediates the effects of perceived autonomy on deep learning.

H4c: 

Intrinsic motivation mediates the effects of perceived competence on deep learning.

3.5. Mediation in the Technological Environment

Based on the online classroom environment, students must overcome the barriers posed by technology, such as the interaction within the teacher–student relationship and the complexity of networked devices [60]. When the technological environment is favorable, students may experience a higher degree of autonomy in interactive teaching and learning activities, and aspects such as smooth internet connections and familiarity with the platform’s technology can enhance close interaction among teachers and peers [61]. Additionally, a favorable technological environment can motivate students to respond to questions more actively and participate enthusiastically in classroom activities [62]. Therefore, this study posits the hypotheses:

H5: 

The technological environment moderates the relationship between interactive teaching and learning and perceived autonomy.

This study first explores the direct impact of online interactive teaching (relatedness) on deep learning. Online interactive teaching serves as the independent variable, with deep learning as the dependent variable. In addition, based on SDT, intrinsic motivation, perceptual ability, and perceptual autonomy are employed as mediating variables. The model is shown in Figure 2.

4. Methodology

4.1. Questionnaire Design and Participants

The questionnaire design followed a structured process. Firstly, we extensively reviewed relevant literature on interactive teaching, SDT, and deep learning, and chose scales compiled or revised by Laird, Koufaris, and other scholars as the foundation of our study. Secondly, we reviewed all the mature scales related to the variables and, in conjunction with expert opinions, revised the questions that were unclear or lacked measurement significance to better align with the study’s requirements. Finally, we developed a more comprehensive questionnaire (

Table 1. Measurement source.

Variable Name	Subject	Source
Online interactive teaching (IL)	1. In the online class, the teacher often participates in our topic discussion and answers our questions on time.	Kuo et al. [63] Wei et al. [64]
	2. In the online class, I and other students are very happy to contribute our learning results and share.
	3. In the online class, the teacher will ask questions timely according to the important and difficult points in the course teaching, and encourage students to actively participate in the communication.
Intrinsic Motivation (IM)	1. In the online class, I think it’s important to have the opportunity to show yourself.	McAuley et al. [65]
	2. In the online class, I think what the teacher teaches is very interesting.
	3. In the online class, I found that interaction with teachers and classmates was not stressful at all.
Perceived Competence (PC)	1. In the study of online classes, I think my expertise has improved.	Gagné [66] Sheldon et al. [67] Fang J. et al. [68]
	2. In the study of online classes, I think I am a capable person.
	3. In the study of online classes, I can complete difficult tasks and plans well.
	4. In the study of online classes, I am pleased with my performance.
Perceived Autonomy (PA)	1. Before going to class, I will preview what I will learn in advance.	Gagné [66] Sheldon et al. [67] Fang J. et al. [68]
	2. In the study of online classes, I will concentrate on the key content of the teacher.
	3. In the study of online classes, I can express my ideas freely.
	4. In the study of online classes, I take the initiative to “raise my hand” to answer questions/ask questions and interact with teachers and classmates.
	5. In the study of online classes, I can learn in the way I think is best for me.
Deep Learning (DL)	1. I can apply what I learned in the classroom to real-world situations.	Laird et al. [69]
	2. I can challenge existing ideas about learning content.
	3. After the teacher raises a question, I usually use a variety of ways of thinking to answer it.
	4. I usually use concept maps, mind maps, and other methods to organize the knowledge I have learned.
	5. I am willing to spend extra time studying online to better understand the knowledge taught by teachers.
Technical Environment (TE)	1. I can skillfully use the functions of the online learning platform.	Koufaris [70]
	2. The quality of the network can ensure that I can interact with teachers and classmates smoothly.
	3. I was pleased with the equipment I was using and the audio and video quality of the online class.

). The questionnaire design consists of two parts: basic personal information and questions related to the research topic. The first part includes personal basic information, such as gender, grade, school location, place of origin, and major classification, with a total of 5 questions. The second part includes questions related to the research topic, divided into 6 constructs with a total of 23 questions. The questionnaire uses a five-point Likert scale for measurement, and participants are required to choose from the following options for each question: “1” means strongly disagree, “2” means disagree, “3” means uncertain, “4” means agree, and “5” means strongly agree (see Appendix A).

The participants primarily come from three universities in Guangzhou, Guangdong Province, China, two universities in Macau, and a few universities in other regions. On the one hand, being a major educational province and the core region of the Guangdong-Hong Kong-Macao Greater Bay Area, Guangdong benefits from its superior geographical location and high level of economic development. Due to numerous well-known universities, high-quality teaching resources, and extensive online teaching experience, the Guangzhou region can offer reliable data for research and contribute to the ongoing innovation and development of the education system. On the other hand, as a multicultural city, Macao possesses unique characteristics and values for the preservation and advancement of multicultural education. This uniqueness can furnish diversified data for the study, enhancing its comprehensiveness. Hence, Guangzhou in Guangdong Province and Macao in China are chosen as the primary research areas for this study, with additional regions selected to ensure a comprehensive representation of the study population. Online questionnaires were distributed to undergraduates with online learning experiences through academic staff or administrators at the surveyed universities. These questionnaires were primarily administered after the commencement of undergraduate classes. To ensure the validity of the survey data, before the formal survey, we carried out detailed questionnaire explanations, filling instructions, and ethical approval statements for the college students who filled out the questionnaire and ensured that participants were informed and consented.

4.2. Data Collection and Analysis

The questionnaire survey primarily utilizes the Questionnaire Star platform, employing network links and two-dimensional codes for distribution. Data collection occurred between November 2023 and December 2023. Out of the 506 questionnaires collected, 121 were identified as having obvious patterns (all items were selected as the same option) or containing invalid answers (responding time less than 120 s or more than 1000 s). After excluding these, 385 valid questionnaires remained, resulting in a response rate of 76.09%, meeting the survey questionnaire’s fundamental criteria.

Data analysis in this study primarily employs partial least squares structural equation modeling (PLS-SEM), a method extensively applied in pedagogical and psychological research. PLS-SEM is adept at handling complex models featuring multiple constructs, indicators, and structural pathways, particularly with small-sample data exhibiting a non-normal distribution. Even with sample sizes below 200 or even 100, PLS-SEM can yield reliable outcomes [71]. PLS-SEM can simultaneously handle formative and reflective constructs, making it conducive to supporting exploratory research and theoretical development [71,72]. Based on the model in this study, the PLS-SEM method is chosen for the following reasons: (1) the model’s complexity, featuring multiple structures, indicators, and pathways; (2) the relatively small sample size of 385 respondents; (3) the non-normal distribution of the study’s data; and (4) the exploratory nature of this research, which seeks to investigate the influencing factors of online interactive teaching on university students’ deep learning. Therefore, it is more appropriate to select PLS-SEM for data analysis and utilize SmartPLS 4 software for model testing, specifically version 4.1.0.0.

5. Results

5.1. Descriptive Statistics

In the initial section of the questionnaire, participants’ basic information was surveyed, and the statistical outcomes are presented in

Table 2. Descriptive statistics.

	Categories	Frequencies	Percentages (%)
Gender	Male	107	27.8
	Female	278	72.2
School Location	Macao	67	17.4
	Guangzhou	305	79.2
	Others	13	3.4
Place of origin	Macao	16	4.2
	Guangzhou	219	56.9
	Others	150	38.9
Grade	Freshman	174	45.2
	Sophomore	43	11.2
	Junior	50	13.0
	Senior	56	14.5
	Others	62	16.1
Major classification	Humanities	72	4.7
	Social Science	18	7.0
	Science departments	27	7.0
	Engineering course	54	14.0
	Medicine	9	2.3
	Education	16	4.2
	Arts	13	3.4
	Management discipline	150	39.0
	Others	26	6.8

. Out of the 385 valid questionnaires received, 305 originated from three universities in Guangzhou, Guangdong Province, 67 from two universities in Macao, China, and 13 from other regions.

5.2. Measurement Model Checking

In

Table 3. Assessment of reflective measurement models.

Constructs	Indicators	Factor Loadings	Cronbach’s Alpha	Composite Reliability (Rho A)	AVE
IL	IL1	0.860	0.824	0.827	0.740
	IL2	0.869
	IL3	0.852
IM	IM1	0.878	0.868	0.868	0.791
	IM2	0.899
	IM3	0.890
PC	PC1	0.880	0.898	0.899	0.766
	PC2	0.886
	PC3	0.870
	PC4	0.864
PA	PA1	0.651	0.860	0.860	0.643
	PA2	0.843
	PA3	0.827
	PA4	0.825
	PA5	0.845
TE	TE1	0.878	0.866	0.866	0.788
	TE2	0.896
	TE3	0.889
DL	DL1	0.833	0.890	0.890	0.694
	DL2	0.835
	DL3	0.844
	DL4	0.826
	DL5	0.826

, Cronbach’s alpha is greater than 0.7, and CR values are greater than 0.7 [73], indicating that the internal consistency test for each latent variable has high reliability and the synthetic reliability test is passed. The AVE values were greater than 0.5 [74], and the loading coefficient values were greater than 0.7, indicating that the data validity test was passed.

Table 4. Fornell–Larcker criterion.

	DL	IL	IM	PA	PC
DL	0.833
IL	0.392	0.860
IM	0.401	0.355	0.889
PA	0.420	0.348	0.444	0.802
PC	0.361	0.351	0.411	0.611	0.875

, Fornell–Larcker criterion indicates that there is a high degree of discriminant validity among the selected potential variables. In

Table 5. Cross-loadings.

	DL	IL	IM	PA	PC	TE
DL1	0.833	0.356	0.291	0.363	0.280	0.312
DL2	0.835	0.311	0.356	0.344	0.349	0.220
DL3	0.844	0.344	0.346	0.336	0.290	0.325
DL4	0.826	0.302	0.348	0.356	0.304	0.297
DL5	0.826	0.322	0.330	0.352	0.282	0.292
IL1	0.321	0.860	0.301	0.319	0.272	0.252
IL2	0.356	0.869	0.321	0.323	0.355	0.267
IL3	0.334	0.852	0.291	0.252	0.274	0.245
IM1	0.326	0.332	0.878	0.392	0.376	0.265
IM2	0.377	0.295	0.899	0.402	0.350	0.293
IM3	0.366	0.319	0.890	0.390	0.371	0.267
PA1	0.328	0.276	0.412	0.851	0.798	0.234
PA2	0.370	0.312	0.345	0.843	0.416	0.345
PA3	0.312	0.253	0.332	0.827	0.345	0.342
PA4	0.291	0.254	0.339	0.825	0.322	0.406
PA5	0.352	0.272	0.301	0.845	0.408	0.330
PC1	0.278	0.269	0.345	0.515	0.880	0.249
PC2	0.315	0.291	0.405	0.547	0.886	0.227
PC3	0.343	0.352	0.371	0.543	0.870	0.245
PC4	0.326	0.315	0.313	0.534	0.864	0.266
TE1	0.297	0.248	0.253	0.360	0.243	0.878
TE2	0.333	0.297	0.290	0.368	0.260	0.896
TE3	0.295	0.244	0.280	0.367	0.247	0.889

, cross-loadings, each indicator’s loading on the relevant constructs is greater than all of its loadings on the other constructs [75]. In

Table 6. HTMT.

	DL	IL	IM	PA	PC	TE
DL
IL	0.458
IM	0.456	0.419
PA	0.472	0.404	0.500
PC	0.403	0.405	0.464	0.650
TE	0.396	0.351	0.357	0.480	0.320

, HTMT, all values shown are less than 0.90. Thus, the reflective measurement model has good discriminant validity.

5.3. Structural Modeling

The collinearity (VIF) and the coefficient of determination (R2 value) are the main metrics used to assess the structural model. The problem of collinearity may exist when the VIF of each indicator is larger than 5. In this study, the VIF for all indicators is less than 5 (refer to

Table 7. VIF.

	VIF
DL1	2.187
DL2	2.172
DL3	2.284
DL4	2.095
DL5	2.098
IL1	1.858
IL2	1.850
IL3	1.873
IM1	2.139
IM2	2.422
IM3	2.278
PA1	1.205
PA2	2.494
PA3	2.713
PA4	2.366
PA5	2.582
PC1	2.663
PC2	2.629
PC3	2.356
PC4	2.362
TE1	2.128
TE2	2.361
TE3	2.265

), indicating the absence of collinearity among constructs [76]. Refer to

Table 8. R² Value.

	R2
DL	0.782
IM	0.762
PA	0.742
PC	0.374

, where the R2 values for explaining the latent variables are all above 0.75, with one close to 0.75 and one close to 0.5, signifying robust explanatory power [75].

In this study, we utilized the constructed theoretical model (Figure 2) and processed the data employing SmartPLS4 software to derive a structural equation model (Figure 3). The graphical output illustrates the six constructs within the reflective measurement model (blue), along with the indicators associated with each construct (yellow), and delineates the relationships between them. The path coefficient, depicted as the numerical value along the arrow, and the Average Variance Extracted (AVE) number situated within each construct are displayed.

The bootstrap algorithm was employed to examine the hypotheses regarding causality between variables, and the significance of their relationship was assessed through the calculation of p-values for each path. The ultimate outcomes of the causality test are delineated in

Table 9. Results of hypotheses testing.

Hypotheses	Relationship	Path Coeffcient	p Values	Condition
H1	IL → DL	0.223	0.000 ***	Support
H2a	IL → PA	0.226	0.000 ***	Support
H2b	IL → IM	0.200	0.000 ***	Support
H2c	PC → DL	0.073	0.185	No Support
H2d	PA → DL	0.210	0.001 ***	Support
H2e	IM → DL	0.199	0.000 ***	Support
H3a	IL → PA → DL	0.138	0.005 **	Support
H3b	PA → PC → DL	0.044	0.190	No Support
H4a	IL → IM → DL	0.040	0.003 **	Support
H4b	PA → IM → DL	0.053	0.003 **	Support
H4c	PC → IM → DL	0.036	0.018 **	Support
H5	TE * IL → PA	0.138	0.001 ***	Support

Note: *, **, and *** represent p values < 0.05, ≤0.01, and ≤0.001, respectively.

.

As shown in Table 9, online interactive teaching (relatedness) has a significant positive correlation with deep learning, perceived autonomy, and intrinsic motivation (H1, H2a, and H2b supported). Perceived competence has no significant effect on deep learning (H2c is not supported). Perceived autonomy and intrinsic motivation are significantly positively correlated with deep learning (H2d and H2e are supported). Since the indirect and direct effects between online interactive teaching (relatedness) and deep learning are significant, perceived autonomy is a partial intermediary between online interactive teaching and deep learning (H3a is supported); intrinsic motivation is a partial mediator between online interactive teaching (relatedness) and deep learning (H4a is supported). Perceived competence has no mediating effect on perceived autonomy and deep learning (H3b is not supported). The Intrinsic motivation mediates the perceived autonomy and deep learning (H4b is supported). The Intrinsic motivation mediates the perceived competence and deep learning (H4c is supported). The technical environment significantly moderates the presence of online interactivity teaching (relatedness) and perceived autonomy (H5 is supported).

6. Discussion and Conclusions

6.1. Discussion

This study finds that online interactive teaching has a significant positive impact on college students’ deep learning (H1). Online interactive teaching integrates interactions between teachers and students, as well as among students themselves aiming to deliver knowledge using multimedia teaching resources available on online platforms, including video, audio, images, etc., to enhance the vividness and interest of learning content. Hence, the utilization of interactive teaching in the online classroom by educators can foster active student engagement through activities such as interactive games and discussions, thereby augmenting students’ interest and learning capabilities, which plays a significant role. Teachers have been demonstrated to serve as more objective predictors of learners’ academic achievement [77], and the communication, guidance, and support they offer enhance students’ thinking. Teachers’ engagement in discussions and guidance for students can not only stimulate students’ deep learning but also serve as significant inspiration [6]. In online learning, after the teacher publishes classroom activities, students at both ends can collaborate remotely using information devices to communicate and collectively discuss the task. This process enables students to gain a better understanding of other students’ learning progress, thereby adjusting their own status in a timely manner and fostering more active learning behaviors. Simultaneously, student interactions can more effectively foster communication and collaboration among students, activate students’ initiative, enhance students’ deep learning, and cultivate their awareness of both competition and cooperation abilities [78]. In conclusion, interactive teaching in online classes significantly influences students’ deep learning.

This study found that intrinsic motivation significantly positively impacts students’ deep learning (H2e). This finding aligns with the conclusions of numerous scholars. Pintrich et al., Rigby et al., and Shiefele concur that intrinsically motivated learners demonstrate a propensity for employing complex reasoning skills and learning strategies, leading to deeper text processing and enhanced conceptual learning [79,80,81]. Students with robust intrinsic learning motivation are predisposed to initiate learning activities and sustain engagement and commitment to the learning process [82], thus enhancing deep learning or conceptual learning and avoiding surface learning [83]. Therefore, the more intrinsically motivated students are, the more they engage in deep learning. While many discussions center on the relationship between intrinsic motivation and deep learning, few studies directly validate this relationship through empirical research methods. Consequently, the findings of this study underscore the significant impact of intrinsic motivation on deep learning.

In the online environment, students’ perceived fulfillment of the three psychological needs, respectively, promotes deep learning through intrinsic motivation. Intrinsic motivation serves as a mediating factor between online interactive teaching (relatedness) and deep learning (H4a). Considering the online format of education, students engage with course materials through screens, foregoing face-to-face interactions with instructors. However, the frequent interaction between teachers and students can alleviate the loneliness of students’ studies to a certain extent [84]. Furthermore, the exchange of resources and mutual assistance among students fosters interpersonal connections, providing a sense of support and care, thereby fulfilling their intrinsic motivation [53]. Therefore, as mentioned above, this study confirms the substantial positive influence of intrinsic motivation on students’ deep learning. The findings indicate that the incorporation of interactive teaching by instructors in online courses represents the manifestation of relatedness within SDT, contributing indirectly to students’ learning outcomes and behavior performance by fostering intrinsic motivation. Consequently, online interactive teaching (relatedness) indirectly impacts students’ deep learning by means of intrinsic motivation.

Intrinsic motivation serves as a mediating factor between perceived autonomy and deep learning (H4b). According to SDT, an increased perception of individual autonomy contributes to the internalization of motivation [85]. In online classes, students are more likely to exhibit intrinsic motivation when they perceive control and ownership of the learning process [21]. Conversely, excessive control over students may diminish their intrinsic motivation to learn, particularly in complex, abstract, and creative learning contexts [86]. As mentioned above, this study finds that intrinsic motivation has a significant positive impact on students’ deep learning. Consequently, students’ perceived autonomy indirectly fosters deep learning by bolstering intrinsic motivation, leading to heightened engagement in online discussions, problem-solving, and knowledge exploration.

Although perceived competence has no direct impact on deep learning (H2c), which means that individuals may overestimate or underestimate their own competence, such students’ perceived competence is not enough to directly affect the process or result of deep learning, which is more dependent on the understanding and application of knowledge and other factors [43], but as mentioned above, this study found that intrinsic motivation has a significant positive impact on students’ deep learning. When individuals have confidence in their own abilities, they tend to show higher enthusiasm to meet learning challenges. This enthusiasm not only drives them to face challenges more bravely, but also enables them to have stronger internal motivation [44], such as improving self-efficacy and enhancing positive emotions. Therefore, perceived competence indirectly affects students’ deep learning (H4c) through intrinsic motivation.

In summary, the study reveals that the three fundamental tenets of SDT—online interactive teaching (relatedness), perceived autonomy, and perceived competence—indirectly influence college students’ deep learning via intrinsic motivation.

The technological environment has a moderating effect on online interactive instruction and perceived autonomy (H5). Compared with traditional offline face-to-face classes, online teaching requires more technical support and management to ensure the smooth progress of the teaching process. In the online environment, the spatial separation of teaching and learning means that teachers and students need to rely on digital tools and platforms to effectively communicate and learn. However, due to this spatial separation, student autonomy becomes a critical factor influencing the effectiveness of online classrooms. When the technological environment is favorable, students may perceive higher levels of autonomy in online learning due to increased choices and control [87], enabling them to actively engage in class interactions with the teacher. Conversely, in unfavorable technological environments, students may perceive limited autonomy due to difficulties in participating smoothly in interactions, following learning plans, etc. [88]. Therefore, when designing online courses, it is essential to fully consider technical and environmental factors to facilitate a more autonomous learning experience for students with adequate technical support.

6.2. Theoretical Contribution

We conclude that this study offers a novel theoretical perspective for deep learning research grounded in SDT. By verifying the influence of interactive teaching, which is represented by relatedness in SDT, on deep learning, the research proves that relatedness has an impact on deep learning and expands the connotation depth of SDT. SDT defines relatedness as the necessity for interpersonal relationships and a feeling of belonging [89]. The most notable disparity between interactive teaching methods and traditional online teaching lies in their focus on improving communication, fostering connections with learners, and encouraging active engagement in online learning activities [90]. Therefore, it is evident that meeting learners’ perceived relatedness is a key characteristic of interactive teaching. Furthermore, the author contends that interactive teaching fulfills the relatedness as outlined in SDT. This study shows that interactive teaching has a significant positive impact on deep learning, that is, relatedness has a significant positive impact on deep learning. Consequently, these findings broaden the theoretical implications of SDT.

This study further validates previous research indicating that the three intrinsic needs do not affect each other sequentially [89] and demonstrates how these needs influence deep learning through intrinsic motivation. To achieve this, the scope of research within SDT is broadened, as is the examination of internal factors influencing deep learning. This study builds upon prior internal research within SDT, showing that intrinsic motivation, perceived competence, and perceived autonomy do not sequentially impact deep learning. Furthermore, the results of this study indicate that intrinsic motivation mediates the influence of the three intrinsic needs on deep learning. This finding enriches and extends the external research factors of SDT, representing a theoretical exploration of its application. Additionally, this study investigates the internal influencing factors and mechanisms of SDT on deep learning, thereby making a significant contribution to theoretical research on the factors influencing deep learning.

This study confirms that the technological environment exerts a regulatory influence on the pathway from “online interactive teaching and learning” to “perceived autonomy,” thereby validating research on the influencing factors of deep learning. Additionally, it offers specific theoretical guidance for establishing an environment conducive to online interactive teaching. Perceived autonomy, recognized as a crucial factor influencing learners’ outcomes in classroom settings [91], cannot be overlooked. Hence, the findings of this study validate the moderating effect, which can inform the design of online interactive teaching. To enhance the promotion of deep learning among learners in online classes, careful attention must be paid to the design of the technical environment. This aspect also lays a theoretical groundwork for establishing a system for the design and development of online interactive teaching in the future.

6.3. Practical Implications

This study has implications for enhancing teaching methods in online instruction among educators and fostering students’ deep learning. Firstly, educators should actively explore effective methods and strategies for facilitating online interactive teaching. For instance, teachers should acquaint themselves with online teaching platforms, interactive classroom tools, etc., which are instrumental in enabling educators to effectively manage and instruct students during the teaching process [92]. Educators can foster students’ interest and engagement through posing questions, facilitating group discussions, and sharing case studies, thereby fostering a conducive and interactive learning environment [92]. Develop various interactive tasks and activities, such as group projects, online games, role-playing scenarios, etc. These tasks and activities can not only increase students’ learning motivation but also help them better understand and apply what they have learned and promote students’ learning and participation [43].

Secondly, educators should consider students’ psychological needs during online teaching, as their perception of these needs directly impacts their learning outcomes [93]. For instance, educators can boost students’ self-efficacy, motivation, and confidence through actionable learning goals, adequate learning resources, and support, as well as by encouraging students to overcome challenges [57]. Educators can assist students in swiftly adapting to online learning environments through clear guidance, fostering a positive learning atmosphere, encouraging the sharing of learning experiences, and promoting experimentation and exploration [94]. Educators can enhance students’ well-being by organizing enjoyable and meaningful learning activities that foster a sense of engagement and involvement [95]. Therefore, educators should comprehensively consider students’ diverse psychological needs in online teaching and implement corresponding measures to effectively promote deep learning and development among college students.

Third, establishing an effective feedback mechanism is necessary to aid teachers in comprehending students’ learning status and issues, thereby enabling adjustments in teaching strategies. Students also require timely feedback from instructors to comprehend their learning status and identify areas for enhancement, thus enhancing teaching quality and effectiveness [96]. Fourthly, institutions of higher education should augment investments in online education platforms and tools, furnish stable and dependable technology, and ensure the smooth progress of online teaching [97].

6.4. Conclusions

Based on empirical research conducted on students’ deep learning in Guangzhou, Guangdong Province, and Macao, China, and the development of a theoretical model grounded in self-determination theory, online interactive teaching satisfies relatedness as the independent variable, intrinsic motivation and perceived autonomy as the mediating variables, and technical environment as the moderating variable in SDT. The study investigated the direct impact of interactive teaching in online classrooms on university students’ deep learning, as well as the influence of students’ perceived psychological needs and intrinsic motivation on promoting deep learning, elucidating the specific process through which online interactive teaching affects deep learning. The research conclusions were derived from data-to-model testing and hypothesis validation. First, interactive teaching in online classes has a positive effect on college students’ deep learning. This suggests that in online classes, instructors utilize interactive teaching methods to encourage active participation among college students [98], thereby fostering deep learning. Second, students’ perceived autonomy and intrinsic motivation play a mediating role in the relationship between interactive teaching and deep learning. Third, intrinsic motivation has an indirect effect on the relationship between interactive teaching (relatedness) and deep learning, intrinsic motivation has an indirect effect on the relationship between perceived autonomy and deep learning, and intrinsic motivation has an indirect effect on the relationship between perceived competence and deep learning, which means that after the three psychological needs of SDT are perceived, it can have an indirect effect on deep learning through intrinsic motivation. Therefore, within the educational context, interactive teaching, as a crucial component of integrating deep learning into online education, requires exploration on how to fully leverage the benefits of interactive communication to foster cooperation and competition, thereby stimulating students’ interest in learning and enhancing their learning effectiveness [99].

7. Research Limitations and Future Recommendations

Based on previous studies, this study discussed the influence of interactive teaching in online classrooms on university students’ deep learning and thoroughly understood the relationship between the two. This study not only enriched the theoretical research on SDT online classrooms but also provided practical ideas for promoting the impact of online teaching on university students’ deep learning abilities. However, due to subjective and objective limitations, this study still has some shortcomings: The coverage of the sample of research subjects needs to be strengthened, and we should continue to expand the scope and number of questionnaires issued to enhance the comprehensiveness and reliability of the results of the data analysis. Respondents generally resist questionnaires with excessive items, leading to a casual attitude towards filling them out. Therefore, questionnaire design considers the respondents’ acceptability with a controlled and simplified number of questions to ensure the quality of questionnaire completion, which may affect the comprehensive content and survey results. Therefore, in the future, it is necessary to continuously improve the means and methods of the study to collect more authentic and accurate data.

There are still undiscovered mediating variables in the relationship between interactive teaching and deep learning. First, this study introduces three psychological needs based on SDT, in which relatedness is replaced by online interactive teaching as independent variables and intrinsic motivation, perceived competence, and perceived autonomy as mediating variables. Intrinsic motivation and perceived autonomy play a mediating role to some extent, but after adding this variable, the direct impact of interactive teaching on deep learning is still significant. These results indicate that there may be other mediating effects from other variables. Secondly, with the development of the internet, whether there is technical support in the online classroom will affect students’ perceptions of the classroom, and these changes are difficult to predict at present [100]. Therefore, future research can explore alternative theoretical frameworks to elucidate the mediating mechanism of interactive teaching in deep learning. Furthermore, conducting comprehensive investigations from diverse perspectives can enhance and broaden the structural model delineating the influence of interactive teaching on college students’ deep learning.