Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Theoretical framework

Educational theories and instructional technology models collectively form the theoretical framework of the adaptive microlearning (AML) system, as illustrated in Fig. 1. These include Constructivism, Connectivism, the Three-Parameter Logistic (3-PL) model, the Adaptive Educational Hypermedia Systems (AEHS) model, and Cognitive Load Theory (CLT). Each theory offers a unique perspective on learning and instruction, contributing to the development of a robust and effective AML system.

As shown in Fig. 1, constructivism and connectivism provide the foundational principles of instructional delivery from a pedagogical perspective. The 3-PL model offers an algorithmic mechanism for assessing learners’ existing knowledge levels and adapting content to individual needs. It works in conjunction with the AEHS model, which links various database elements, to provide a structural framework supporting personalized and adaptive learning pathways. With the aid of CLT, the AML system not only focuses on personalized learning but also ensures that learners’ cognitive resources are optimally managed, preventing cognitive overload and enhancing learning efficiency. CLT plays a critical role in regulating the mental effort required from learners, especially in complex or new learning environments, thereby aligning well with adaptive learning principles. Supported by CLT, the AML system ensures that learning materials are presented in a manner that optimizes cognitive resources and prevents overload, further enhancing learning adaptability.

Constructivism

Constructivism asserts that learning involves actively constructing knowledge rather than merely acquiring it³⁰. Constructivism posits that learners are active constructors of knowledge, not passive receivers. Additionally, constructivism advocates for learners to be active participants in the learning environment³¹. The learning environment should be learner-centered, allowing learners to independently control their learning process. Constructivism emphasizes that learners construct their own understanding and knowledge of the world through experiences and reflection³². In adaptive microlearning, learners are encouraged to build on their existing knowledge while managing cognitive load. Specifically, adaptive microlearning enables learners to interact with content tailored to their current understanding levels, promoting active learning and critical thinking without overwhelming them with excessive cognitive demands.

According to constructivism, learning should be autonomous and not dictated by “one-size-fits-all” requirements. Therefore, adaptive microlearning, guided by constructivism, shifts from conventional “diffuse irrigation” to “precise drip irrigation,” supporting more individualized learning. It supports customized learning, offers different learning paths, and guides learners to complete their learning through exploration and experience³³. In this process, CLT complements constructivism by ensuring that the learning paths do not impose unnecessary extraneous cognitive load, thereby fostering meaningful engagement and deeper reflection.

Connectivism

Connectivism theory posits that learning involves building a connecting knowledge network³⁴, signifying distributed and expandable knowledge, thus explaining how learning occurs in the digital age. The key elements of connectivism include “nodes,” “connections,” “networks,” and “knowledge flow,”^35,36 as shown in Fig. 2. Connectivism asserts that learning is a dynamic process involving the connection of scattered knowledge nodes to form paths, ultimately creating a knowledge network³⁷.

Schematic diagram of the learning process of connectivism theory.

According to connectivism, learning occurs through the formation and navigation of networks where knowledge is distributed across connections and learners actively engage in creating and traversing these networks³⁶. In conventional microlearning, learning content consists of small, dispersed, and interrelated modules. Adaptive microlearning aims to integrate these loose knowledge units into a personalized learning network for each learner³⁸. Learners form meaningful knowledge networks by linking knowledge units scattered across different nodes, facilitating a holistic and integrated learning experience. In the context of cognitive load theory, connectivism can be further enhanced by managing learners’ cognitive load as they connect new knowledge nodes to their existing networks. The AML system developed in this study associates and reorganizes learning content into fragmented knowledge units, aligning perfectly with connectivism theory. As learners attempt to integrate new knowledge within the adaptive microlearning system, CLT ensures that expanding these networks does not exceed the learner’s cognitive capacity. Using databases and algorithms, learners are recommended knowledge units aligned with their existing knowledge level, helping them clarify their next learning tasks. This ensures a smoother knowledge acquisition process while minimizing cognitive overload, thereby reinforcing both learning adaptability and long-term knowledge retention.

Cognitive load theory

Cognitivism focuses on the inner mental activities of learners and argues that learning is an information processing process. Cognitive load is the total amount of cognitive resources required by individuals during information processing¹⁴. Cognitive Load Theory (CLT) serves as a critical component of the AML system. While constructivism and connectivism emphasize active learning and network building, CLT focuses on managing the mental effort involved in these processes. CLT distinguishes among three types of cognitive load: intrinsic, extraneous, and germane.

Intrinsic cognitive load is inherent to the learning content and is related to its complexity and the learner’s prior knowledge³⁹. It is determined by the interaction between the learning content and the learner’s existing knowledge. When the learning content is overly complex and difficult, exceeding the learner’s existing knowledge capacity, intrinsic cognitive load increases⁴⁰. In the AML system, intrinsic load is managed by adjusting the content complexity to match the learner’s current knowledge level, ensuring that learners are not overwhelmed by overly complex tasks.

Extraneous cognitive load is the unnecessary cognitive load caused by improper instructional design. It primarily arises from the organization and presentation of learning content²⁴. These redundant designs consume extra cognitive resources, forcing learners to perform activities unrelated to learning, thereby increasing extraneous cognitive load⁴¹. Extraneous cognitive load can be reduced by improving how learning content is presented. In adaptive systems like AML, this type of load is minimized by presenting content in a clear, organized manner, thus allowing learners to focus on the core learning material. Reducing extraneous load is crucial to freeing up mental resources for actual learning.

Germane cognitive load refers to the cognitive resources a learner invests in learning, influenced by the learner’s motivation and effort⁴². This type of cognitive load is positive. Increasing germane cognitive load effectively promotes learning and can enhance learners’ efficiency. By tailoring content complexity and presentation, the AML system optimizes germane load to help learners focus on constructing and internalizing new knowledge.

Cognitive load theory assumes that intrinsic cognitive load is fixed, while extraneous and germane cognitive loads can be managed through better instructional design⁴³. Effective instructional design should minimize extraneous cognitive load, manage intrinsic cognitive load, and optimize germane cognitive load. The AML system aims to balance these types of cognitive load by tailoring content complexity and presentation to individual learner needs, ensuring that learners engage meaningfully with the content without feeling overwhelmed.

While constructivism and connectivism guide the overall learning philosophy, CLT serves as the backbone for managing the delivery of learning in practice. Studies show that high levels of extraneous cognitive load can impede learning by overwhelming cognitive resources, while appropriate levels of intrinsic and germane cognitive load can enhance understanding and retention¹⁴. Given its significant impact on learning outcomes, cognitive load was selected as a variable to evaluate the effectiveness of the AML system. The adaptive nature of the AML system continuously adjusts the learning content to suit learners’ cognitive capacity, preventing overload and facilitating deeper learning and better retention.

Learning adaptability

Learning adaptability refers to a learner’s ability to actively self-adjust and respond to changes in the environment to achieve better learning outcomes²⁶. In complex learning environments, learners must adjust their mentality and behavior to adapt to changes and respond positively to feedback to persist in learning⁴⁴. Learning adaptability is essential for achieving academic performance and personal development⁴⁵, especially in online microlearning environments filled with novelty, variability, and uncertainty.

Learning adaptability can be summarized with two key concepts: “balance” and “flexibility.” “Balance” refers to the ability to self-adjust when faced with sudden changes, while “flexibility” refers to the capacity to respond positively to these changes⁴⁶. Learning adaptability allows learners to balance their interactions with the learning environment⁴⁷, thereby maintaining positive physiological and psychological states. This balance is crucial for enhancing learners’ online learning experiences, helping them remain calm and focused amid the dynamic nature of online microlearning.

The AML system is designed to address the unique challenges faced by in-service personnel, particularly the conflict between work and learning and the uncertainty regarding time and place of learning. By incorporating adaptability into the learning design, these systems better support learners in managing their educational pursuits alongside professional responsibilities. The AML system enhances learning adaptability by offering adaptive learning paths and content recommendations that evolve with the learner’s progress. This adaptability helps learners better manage their learning processes, making adjustments based on real-time feedback and system suggestions, which is essential for effective lifelong learning and professional development. Additionally, learning adaptability, as a measurement variable, effectively observes and assesses learners’ psychological characteristics and abilities to cope with complex and fragmented learning environments.

Three-parameter logistic model

Item Response Theory (IRT) asserts that a learner’s existing knowledge is an invisible psychological trait reflected through test items⁴⁸. IRT is based on two principles. First, in actual tests, learners’ test scores are closely related to their potential traits⁴⁹, which can be measured in an adaptive microlearning system through quiz sessions. The potential traits in the quiz reflect the learner’s existing knowledge level, measured as an unknown value “θ.” Additionally, learners’ existing knowledge levels can be predicted and modeled based on their quiz responses.

The Three-Parameter Logistic (3-PL) model proposed by Birnbaum is a dichotomous model considering only two possible responses to an item: correct or incorrect. The algorithmic formulation of the 3-PL model is as follows:

“P(u_i = 1/θ)” indicates the probability that a learner with an existing knowledge level θ can answer item i correctly. “P(u_i = 0/θ) = 1 – P(u_i = 1/θ)” indicates the probability that a learner with an existing knowledge level θ answers item i incorrectly. “u_i” indicates the learner’s response to item i. “u_i = 1” indicates the learner answered item i correctly. “u_i = 0” indicates that learner answered item i incorrectly. “θ” indicates the learner’s existing knowledge level, which can be measured by testing. The theoretical value range of the existing knowledge level θ is [-∞, +∞]. However, in practice, the generally considered range is [-4.0, + 4.0] or [-3.0, + 3.0]⁵⁰.

“a_i” represents discrimination, referring to the test item’s ability to distinguish learners’ potential characteristics. The higher the item discrimination value, the better it distinguishes learners with different existing knowledge levels.

“b_i” indicates difficulty, referring to the test item’s difficulty level. The more difficult the item, the less likely the learner is to get it right. Conversely, the less difficult the item, the more likely the learner is to answer it correctly. The value range of the difficulty parameter is the same as the knowledge level range.

“c_i” represents the guessing coefficient, referring to the probability that a learner can correctly answer the item through random selection without any knowledge of the item.

The 3-PL model provides a scientifically defensible algorithm for assessing learners’ existing knowledge levels. The AML system assumes that parameters (including a_i, b_i, and c_i) are tested and determined in advance and only needs to consider how to select items and calculate the existing knowledge levels of learners. The AML system selects quiz questions for specific knowledge and calculates the learner’s existing knowledge level. The application of the 3-PL model helps select appropriate quiz items matching the learner’s proficiency, thereby minimizing unnecessary cognitive load and enhancing learning adaptability. By accurately measuring the learner’s knowledge state, the AML system can provide targeted content that supports effective learning and reduces the chances of overwhelming the learner with content that is either too difficult or too easy.

Model of adaptive educational hypermedia systems

The adaptive educational hypermedia systems (AEHS) model proposed by Brusilovsky consists of five core components: a domain model, a learner model, a pedagogical model, an interface module, and an adaptive engine⁵¹. The database mechanisms of the adaptive microlearning system in this study use the AEHS model as a reference for system design. The reference model uses established standards and norms to guide system development, accurately defining the roles and responsibilities of each system component^52,53. Figure 3 illustrates the database architecture of the AML system, which is based on a variant of the AEHS model.

Database architecture of the adaptive microlearning system.

The domain model is a database that stores all the learning content in the system⁵⁴. It describes the knowledge nodes involved in the application field and their relationships. The learning content in the domain model is categorized into “courses,” “lessons,” and “knowledge units.” A “knowledge unit” is the smallest component of learning content. Knowledge units are grouped into “lessons,” and all lessons together form “courses.”

The learner model records the learner’s demographic information, interaction data, learning progress and history, and knowledge state and proficiency levels, as shown in Table 1. In the learner model of the AML system in this study, demographic information is static and obtained during the registration, while other information is dynamic and automatically collected by the system during the learning process.

The pedagogical model defines regulations and standards to achieve specific learning goals during the learning process^55,56. It specifies how to modify and update the learner model and extract learning content from the domain model for different learning sessions based on information recorded in the learner model⁵⁷. The design of the pedagogical model in this adaptive microlearning system is guided by the domain model and the learner model. It makes decisions on updating the learner model, presenting learning content, and providing system feedback^56,58.

The interface module serves as the medium for learners to interact with the learning system⁵⁹. It provides interactive interfaces and presents various sessions and functions based on information from the domain model, learner model, and pedagogical model. Different visual interfaces and presentation content create varied learning experiences for learners⁶⁰.

In summary, integrating educational theories and instructional models in the theoretical framework creates a synergistic relationship that enhances the effectiveness of the AML system. Specifically, constructivism and connectivism emphasize the initiative and networked nature of learning, providing guidance on the architecture of the learning environment and the delineation of learning resources. Cognitive load theory is used to manage learners’ cognitive resources, ensuring that the learning material is neither too simple nor too complex. This balance is essential for maintaining learner engagement and promoting deep learning. The algorithms in the 3-PL model are implemented in the AML system, allowing it to tailor quiz item difficulty to match the learner’s current knowledge level. This adaptability ensures learners are continually challenged at an appropriate level, enhancing both learning effectiveness and learner satisfaction⁶¹. The AEHS model integrates these elements into a cohesive system, creating a flexible and responsive learning environment. Learning adaptability, a key outcome of this integration, is crucial for learners to navigate and succeed in dynamic and changing environments.

The academic support and persuasiveness of the AML system are strengthened by integrating theories and models from the theoretical framework, ensuring the AML system is robust and flexible. The AML system responds to the diverse needs of learners in dynamic learning environments and assists in-service personnel in developing the skills necessary for career advancement, thereby sustaining effective lifelong learning.