1. Introduction

We are situated in a new era driven by data. Big data technology, through the collection, storage, analysis, and visualization of massive information, reveals patterns and correlations difficult to discern through traditional methods, bringing disruptive changes across various sectors. In the field of education, this "big data storm" has similarly arrived, giving rise to the new paradigm of "smart education." Student learning behaviors, interaction processes, and assessment outcomes are being digitized, forming valuable "educational big data." This data is no longer merely a by-product of instruction but has become a strategic resource for optimizing teaching processes, enabling personalized learning, and enhancing educational quality. Against this macro-background, the roles and competency structures of university teachers face unprecedented challenges and opportunities. The traditional teaching model centered on "the teacher, the textbook, and the classroom" struggles to meet the learning needs of digital native students. Mastery of basic skills such as presentation software or online platform operation is no longer sufficient to meet the teaching demands of the big data era. Teachers need to develop a deeper, more systematic capability—the ability to understand, utilize, and critically reflect upon big data and related technologies to enhance teaching effectiveness, promote academic research, and lead educational innovation. This capability is termed "the core competency in educational technology for the big data era."

Currently, although many universities have initiated educational technology training for teachers, prevalent issues include outdated training content focused primarily on tool usage, a lack of cultivation of data thinking and integrative innovation abilities, singular training methods often divorced from practice, and an absence of systematic top-level design and supportive environments. These shortcomings result in insufficient motivation for teacher learning and difficulty in internalizing technology into regular teaching practices. Therefore, systematic research on the constituent elements of this core competency for university teachers in the big data era, and the subsequent design of scientific and effective development pathways, holds significant theoretical value and practical urgency. It is not only crucial for promoting the connotative development of higher education informatization but also an inevitable requirement for improving talent cultivation quality and realizing educational modernization.

2. The Composition of Core Educational Technology Competencies for University Teachers in the Big Data Era

Core competency refers to the essential competencies and fundamental qualities that individuals must acquire to adapt to social changes and meet personal development needs throughout lifelong learning and holistic development ^[1]. The core competency in educational technology for university teachers is not a static or singular list of skills but rather a dynamically evolving, multi-layered, and interconnected complex of abilities. It transcends the "technology as tool" theory, emphasizing the integrated quality of utilizing technology—particularly big data technology—to solve teaching problems, optimize learning experiences, and create educational value within authentic and complex educational scenarios. This study synthesizes its core composition into the following five key dimensions:

2.1 Data-Driven Teaching Decision-Making Ability

This forms the cornerstone of teacher competency in the big data era. It requires teachers to regard data as a "compass" for teaching improvement, rather than relying solely on experiential intuition. Firstly, teachers should possess keen data awareness and interpretation skills, enabling them to identify which teaching segments generate valuable data and understand the educational significance behind this data. For instance, discovering a high replay rate for a specific instructional video via Learning Management System (LMS) data might indicate that the concept is a common difficulty for students. Secondly, teachers need to master basic data analysis skills, allowing them to use common tools to perform preliminary analyses of student learning progress, engagement levels, etc., thereby forming holistic and individualized insights into student learning status. Ultimately, the value of this ability is realized when teachers can promptly adjust teaching strategies—such as modifying the instructional pace or designing targeted interventions—based on evidence from data analysis, thus achieving a shift from "experience-driven" to "data-driven" teaching cycles.

2.2 Capacity for Creating Personalized Learning Environments

The core value of big data technology lies in its support for scaled personalized education. Teachers need to transition from uniform transmitters of knowledge to architects of personalized learning environments. This requires teachers to leverage adaptive learning platforms or intelligent teaching systems to design or recommend personalized learning pathways and resource sequences based on students' prior knowledge levels and real-time performance, enabling "customized" learning experiences. Simultaneously, after identifying student groups with diverse needs based on data, teachers should be able to design differentiated learning tasks and intervention strategies, implementing early warnings for students at potential academic risk. Furthermore, flexibly utilizing technologies like online collaboration tools and virtual simulation experiments to design open, inquiry-based learning activities is also key to meeting students' multiple intelligences and cultivating higher-order thinking skills.

2.3 Competence in Human-AI Collaborative Smart Teaching

Artificial Intelligence (AI), as an advanced application of big data technology, is entering the classroom. Teachers must learn to collaborate with "AI teaching assistants," leveraging their respective strengths. This implies that teachers should be familiar with and capable of effectively evaluating various AI educational tools, such as automated grading systems or AI language partners, and integrating them organically into the teaching workflow, thereby freeing themselves from repetitive tasks. More critically, teachers need the wisdom to design effective human-AI task division, clearly defining which tasks are suitable for machine handling (e.g., knowledge transmission, data monitoring) and which core responsibilities must remain with the teacher (e.g., emotional support, value guidance, complex problem-solving). Throughout this process, teachers must consistently maintain a humanistic teaching orientation, ensuring that technology application serves the holistic development of students, remaining vigilant against the potential alienation technology might cause, and upholding the fundamental purpose of education.

2.4 Technology-Enabled Academic Innovation Capability

University teachers bear the dual mission of teaching and research, and big data technology is similarly revolutionizing research paradigms. In educational research, teachers can utilize educational big data to conduct data-informed action research, analyzing student learning behavior data before and after teaching interventions to validate the effectiveness of specific pedagogical methods, thereby producing high-quality educational research outcomes that, in turn, inform teaching practice. Within their respective disciplinary research fields, teachers with solid data literacy are better equipped to utilize domain-specific big data for scientific discovery and innovation, while also integrating this cutting-edge research data thinking and methodology into daily teaching, subtly fostering students' research potential and innovative spirit.

2.5 Ethical Reflective Capacity Regarding Educational Big Data

As the scope and depth of data collection expand, issues of data security and ethics are becoming increasingly prominent. This constitutes an indispensable critical dimension of the teacher's core competency.With artificial intelligence deeply integrated into the educational process, accountability often becomes blurred when educational errors or incidents occur ^[2]. Firstly, teachers must establish a firm awareness of data security and privacy protection, strictly adhering to data ethics norms and ensuring the security of students' personal information and learning data. Secondly, they need a critical awareness of algorithmic fairness, enabling them to scrutinize potential biases and discrimination embedded in educational algorithms, thus avoiding the harm that "data hegemony" could inflict on educational equity. Ultimately, teachers should develop a dialectical thinking ability regarding technology application, constantly reflecting in practice on fundamental questions such as whether technology genuinely enhances learning outcomes or potentially widens the digital divide, thereby making responsible and ethically sound technological choices.

These five dimensions are interconnected and mutually reinforcing, collectively forming a "pyramid" model of core educational technology competencies for university teachers in the big data era. Data-driven decision-making ability is the foundation; creating personalized environments and human-AI collaborative teaching are the core applications; academic innovation capability represents the extension; and ethical reflective capacity serves as the pervasive "ballast" and "guiding compass."

3. Development Pathways for the Core Educational Technology Competencies of University Teachers

Having constructed the core competency framework, translating it into tangible teacher capabilities requires a systematic and sustainable development system. This system should break away from the traditional "one-off training" model and shift towards an "empowerment and support" model that spans the entire teacher career lifecycle.

3.1 Fostering a Data-Driven Institutional Culture through Strategic Top-Level Design

Any effective change begins with the acceptance of ideas and institutional guarantees. At the institutional level, there is a primary need to strengthen organizational leadership and strategic planning, integrating the enhancement of teachers' core educational technology competencies into the overall plan for university informatization development and faculty team building. This involves establishing a cross-departmental collaborative task force led by senior administration to formulate clear medium- and long-term development goals. Secondly, it is essential to actively cultivate an organizational culture of "speaking with data and teaching based on evidence" across the university through means such as disseminating best practices and organizing teaching innovation competitions, making data literacy a professional instinct for teachers. Finally, relevant incentive policies and standards must be formulated, explicitly listing core educational technology competencies as important criteria for faculty appointment, professional title evaluation, and performance assessment, thereby guiding and stimulating teachers' intrinsic motivation to proactively enhance their capabilities from an institutional level.

3.2 Establishing a Tiered and Categorized Training System for Targeted Empowerment

Given the diversity in teachers' disciplinary backgrounds, technological proficiency, and career stages, it is imperative to construct a tiered and categorized training system for targeted empowerment. Regarding tiers, universal training on foundational literacy can be provided for all faculty, focusing on cultivating data awareness and basic tool usage. Enhanced training on integration and application can be offered for interested key teachers, delving into data analysis tools and personalized teaching strategies. Concurrently, a cohort of "seed teachers" can be selected and nurtured as an innovation leadership tier, supporting them in conducting cutting-edge pedagogical action research and leveraging their radiating influence. Regarding categorization, training content and cases tailored to the characteristics of different disciplines (e.g., humanities, STEM) should be designed, allowing teachers to intuitively perceive the close relevance of technology to their own teaching. Simultaneously, differentiated learning objectives and flexible assessment methods should be set for teacher groups at different career stages, such as newly hired faculty and mid-career professional teachers.

3.3 Building Practice-Oriented Teacher Learning Communities for Internalization through "Learning by Doing"

The genuine internalization of knowledge is inseparable from practice, reflection, and collaboration in authentic contexts. An effective pathway is implementing a project-based approach for "technology-supported teaching reform," encouraging teachers to apply for projects in teams with the requirement that projects must integrate big data or intelligent technologies, while the teacher development center provides technical support and expert guidance. Simultaneously, effort should be made to establish interdisciplinary teacher practice communities, regularly organizing salons, demonstration lessons, and case study seminars, allowing teachers from different disciplines, educational technology experts, and data analysts to share experiences and solve problems together, forming a virtuous ecology of "peer support and expert guidance." Furthermore, it is crucial to develop and promote a series of "Smart Teaching Demonstration Classrooms." These spaces will allow teachers to engage in hands-on experience and practice, enabling them to intuitively perceive the profound transformations in teaching models empowered by technology.

3.4 Implementing Data-Informed Evaluation and Incentive Mechanisms to Form a Continuous Improvement Loop

Scientific evaluation is key to driving the continuous optimization of the development system. There is a need to construct a multidimensional developmental evaluation system, moving away from past singular evaluation standards to instead encompass multiple dimensions such as teaching design, classroom practice, student evaluation, and teaching research outcomes, utilizing teaching portfolios to record the entire process of teacher growth ^[3]. Learning analytics technology can be introduced to track and analyze data on teacher participation in training, platform usage, and other behaviors, thereby identifying their growth trajectories and challenges and providing personalized feedback and support suggestions. Regarding incentives, their targeting and attractiveness should be enhanced. For teachers who achieve remarkable results in technology integration, rewards should not only be material but also include developmental opportunities such as domestic and international visiting scholar programs and participation in high-level conferences, thereby stimulating their intrinsic achievement motivation.

3.5 Ensuring Comprehensive Resource and Service Support to Build a Solid Foundation

Even the best ideas and plans will remain castles in the air without resource and service support. The primary task is to build an integrated, intelligent teaching platform that consolidates core functions like course development, learning management, data analysis, and resource push, creating a user-friendly, interoperable one-stop working environment that fundamentally lowers the technical threshold for teachers. Secondly, it is necessary to provide abundant high-quality digital educational resources and tool libraries, including institutional open online courses, virtual simulation experiments, and academic databases, while equipping teachers with legitimate, easy-to-use software tools and timely technical support services. Ultimately, establishing a permanent teaching technology support center is vital. A professional support team composed of educational technology specialists, instructional designers, and IT engineers should be formed to provide "full-chain" consulting services for teachers—from course design and technical implementation to effect evaluation—becoming a strong backing for teachers exploring teaching innovation.

4. Conclusion

The advent of the big data era presents university teachers with both severe challenges and a historic opportunity for professional leapfrogging development. The core competency model constructed in this study, supported by the five pillars of "data-driven decision-making, environment creation, human-AI collaboration, academic innovation, and ethical reflection," represents a systematic attempt to address the demands of this era. Matching this model is a systematic cultivation project requiring collaboration and joint efforts from governments, universities, faculties, and individual teachers. The road ahead remains long and fraught with change. Universities must, with foresight and firm determination, place the cultivation of teachers' core educational technology competencies at a strategic height. Through conceptual guidance, institutional guarantee, practical empowerment, and environmental support, they can assist the broad body of teachers in successfully completing the role transition from "knowledge transmitters" to "learning designers," "growth partners," and even "leaders of educational innovation." Only in this way can higher education firmly grasp the opportunities presented by big data and cultivate innovative talents capable of adapting to and leading future society.

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