Complex Learning Research Articles

Developing Online Tutorials Through Peer-Assisted Learning (PAL)

The Four-Component Instructional Design (4C/ID) model facilitates complex learning, while Peer-Assisted Learning (PAL) offers a robust framework for collaborative acquisition of knowledge and skills. This study investigates how a collaborative environment, integrated within the 4C/ID framework, can enhance the effectiveness of online tutorial development by learning designers. Employing a qualitative approach, we explored the underlying mechanisms of the design process. Preliminary findings suggest that integrating PAL with 4C/ID improves the quality and engagement of online educational content. This research suggested the potential of collaborative strategies in instructional design, proposing a model that could be beneficial for enhancing future educational practices.

Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach

Abstract This study explored the application of black box machine learning (ML) to build high throughput models that predict the creep response of Ni-based Alloy 617. Black box ML refers to highly complex machine learning algorithms that generate outputs from inputs without an interpretable internal process. The rapid implementation of suitable heat of alloys into targeted service is impeded by the extended qualification process involving chemistry-processing-structure-performance assessment. The ASME B&PV Code III outlines the requirement of 10,000 h of creep testing before each heat can be qualified for service and 30,000 h for heats that exhibit metastable phases. There is a critical need to shorten the development-to-deployment timeline for heats of an alloy at specific applications. Recent advancement in ML offers the ability to identify correlations in data which is difficult to elucidate by other approaches. To that end, black box ML is employed to expedite the HEAT qualification of Alloy 617 and predict performance from HEAT chemistry out to an unprecedented timescale. In this study, creep data for Ni-based Alloy 617—a solid solution strengthened material is gathered from a wide range of data sources. The alloy chemistry, phases, precipitates, and microstructural features are analyzed to obtain the key strengthening mechanism. Service conditions, mechanical properties, chemistry, and chemical ratios are provided as potential input features. The Pearson correlation coefficient with a 95% confidence bound is employed for input feature screening where poorly correlated inputs are eliminated to speed up the ML process and prevent under- and/or over-fitting of predictions. In the ML algorithm, the selected input features are regarded as predictors, and rupture is regarded as the response. An algorithm evaluation is performed where 20 ML algorithms are trained with the training set. The three-layered neural network (NN) was observed to be the best algorithm for the given data based on statistical rationale. The NN accurately predicts rupture across a range of isotherms and data sources. The interpolative and extrapolative predictions are in compliance with ECCC V5 guidelines. A post-audit validation exhibits neither under- nor over-fitting and confirms the applicability of NN algorithms to unseen data. The black box ML provides a pathway to predict the performance directly from chemistry and opens avenues to rapid heat qualification.

A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020

In epidemiological research, accurate estimation of historical ground-level ozone (O3) concentrations with enhanced spatiotemporal resolution is crucial for effective exposure assessment. The current state-of-the-art for estimating air pollutant concentrations is a two-stage ensemble method that integrates outputs from multiple machine learning algorithms. Despite its effectiveness, opportunities exist to refine this approach for more precise O3 estimation. In this study, we propose an enhanced ensemble method that incorporates four key strategies. First, we employ high-resolution spatiotemporal predictors derived from prior machine learning studies for refined secondary learning. Second, we use sophisticated algorithms, including categorical gradient boosting, deep neural network, random forest, stochastic variable Gaussian process, transformer, and a combination of convolutional neural network and long short-term memory neural network, as sublearners to enhance learning capabilities. Third, we spatiotemporally split the sample set and then train submodels separately on each subset to eliminate the unobserved spatiotemporal heterogeneity. Finally, we apply a complex machine learning algorithm, rather than the generalized additive model, for integrating sublearner predictions, enabling the capture of intricate nonlinear relationships beyond basic spatiotemporal linear weights. To validate these improvements, we estimated daily maximum 8-h moving average O3 concentrations ([O3]MDA8) across Chinese mainland from 2013 to 2020 at a 1 km spatial resolution. The proposed method demonstrated notable accuracy, achieving an out-of-station determination coefficient (R2) of 0.943 and a root-mean-square error (RMSE) of 10.197 μg/m3. This performance marks a nearly 15% improvement over the best existing Chinese O3 exposure model based on a single algorithm and also surpasses previous studies utilizing traditional ensemble methods for other air pollutants. Our enhanced ensemble approach significantly bolsters the reliability and robustness of future environmental epidemiological studies by further mitigating “misclassification” errors.

Interpartner learning capabilities and relationship performance during complex projects

Purpose Success in projects requires understanding and managing increasing complexity. This study aims to address the gap in the literature regarding the relationship between project complexity and various forms of interpartner learning capability. In addition, the authors explore the moderating effect of supplier design responsibility on the relationship between project complexity and interpartner learning capability. Design/methodology/approach From an electronics supplier’s perspective, the authors propose that the effect of project complexity is a process of knowledge acquisition and sharing that is facilitated by various forms of interpartner learning capability, including absorptive learning and joint learning, with the upshot of fostering name-brand customer dependence in international exchange relationships. A questionnaire survey is used to collect data from project, product and account managers in the electronics manufacturing industry. The conceptual model is tested using 226 returned questionnaires. Findings The results indicate that complex projects can drive absorptive learning and joint learning capability, fostering enhanced customer dependence and relationship performance. Further, supplier design responsibility has a positive moderating effect on the relationship between project complexity and joint learning capability. However, project complexity is not significantly moderated by the effect of supplier design responsibility on absorptive learning capability. Originality/value Complexity fosters behaviors that influence interpartner learning, which highlights the connection between project management complexity and organizational learning in theory and practice.

Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects. A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attributable to operator inexperience, and adjust for these alongside patient factors in evaluating device safety signals. To maintain integrity, we employed blinding between data generation and analysis teams. Synthetic data used underlying distributions and patient feature correlations based on clinical data from the Department of Veterans Affairs between 2005 and 2012. We generated 2494 synthetic datasets with widely varying characteristics including number of patient features, operators and institutions, and the operator learning form. Each dataset contained a hypothetical study device, Device B, and a reference device, Device A. We evaluated accuracy in identifying learning effects and identifying and estimating the strength of the device safety signal. Our approach also evaluated different clinically relevant thresholds for safety signal detection. Our framework accurately identified the presence or absence of learning effects in 93.6% of datasets and correctly determined device safety signals in 93.4% of cases. The estimated device odds ratios' 95% confidence intervals were accurately aligned with the specified ratios in 94.7% of datasets. In contrast, a comparative model excluding operator learning effects significantly underperformed in detecting device signals and in accuracy. Notably, our framework achieved 100% specificity for clinically relevant safety signal thresholds, although sensitivity varied with the threshold applied. A machine learning framework, tailored for the complexities of post-market device evaluation, may provide superior performance compared to standard parametric techniques when operator learning is present. Demonstrating the capacity of ML to overcome complex evaluative challenges, our framework addresses the limitations of traditional statistical methods in current post-market surveillance processes. By offering a reliable means to detect and adjust for learning effects, it may significantly improve medical device safety evaluation.

TargetCall: eliminating the wasted computation in basecalling via pre-basecalling filtering.

Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, that is, reads. State-of-the-art basecallers use complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally inefficient and memory-hungry, bottlenecking the entire genome analysis pipeline. However, for many applications, most reads do not match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCall's key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads, and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. Our thorough experimental evaluations show that TargetCall 1) improves the end-to-end basecalling runtime performance of the state-of-the-art basecaller by while maintaining high recall in keeping on-target reads, 2) maintains high accuracy in downstream analysis, and 3) achieves better runtime performance, throughput, recall, precision, and generality than prior works. TargetCall is available at https://github.com/CMU-SAFARI/TargetCall.

A study of comparison of perioperative complications in intraperitoneal onlay mesh technique vs extended view totally extraperitoneal ventral hernia repair

Background: Abdominal hernia occurs through the layers of the abdominal wall at a weak point. Laparoscopic ventral hernia repair (LVHR) is an established treatment for ventral hernias. This study was conducted in patients who underwent e-TEP and IPOM surgery for ventral hernia to characterize postoperative pain, recovery time, and quality of life. Predefined preoperative and peri-operative factors were examined for their potential impact. Methods: A prospective study on a total of 50 patients who underwent IPOM and e-TEP for ventral hernia was conducted, who fulfilled the inclusion criteria. Patients were randomized by simple random sampling technique and were divided into two groups of 25 each (Group A- e-TEP and Group B- IPOM). Data of both groups were compared and analysed for statistical significance using Chi square test and Student ‘t’ test. Results: The eTEP approach provides benefits compared to IPOM in terms of reduced hospital stay, earlier return to work and cost effective. However, eTEP is linked to complex learning process and in the end the decision of the surgery type should be made on the basis of specific patient’s conditions, surgeon’s expertise and availability of resources Conclusions: In study we found that both surgeries were comparable, with each having some benefits over the other.

Cleft Lip and Palate Classification Through Vision Transformers and Siamese Neural Networks

This study introduces a novel approach for the diagnosis of Cleft Lip and/or Palate (CL/P) by integrating Vision Transformers (ViTs) and Siamese Neural Networks. Our study is the first to employ this integration specifically for CL/P classification, leveraging the strengths of both models to handle complex, multimodal data and few-shot learning scenarios. Unlike previous studies that rely on single-modality data or traditional machine learning models, we uniquely fuse anatomical data from ultrasound images with functional data from speech spectrograms. This multimodal approach captures both structural and acoustic features critical for accurate CL/P classification. Employing Siamese Neural Networks enables effective learning from a small number of labeled examples, enhancing the model’s generalization capabilities in medical imaging contexts where data scarcity is a significant challenge. The models were tested on the UltraSuite CLEFT dataset, which includes ultrasound video sequences and synchronized speech data, across three cleft types: Bilateral, Unilateral, and Palate-only clefts. The two-stage model demonstrated superior performance in classification accuracy (82.76%), F1-score (80.00–86.00%), precision, and recall, particularly distinguishing Bilateral and Unilateral Cleft Lip and Palate with high efficacy. This research underscores the significant potential of advanced AI techniques in medical diagnostics, offering valuable insights into their application for improving clinical outcomes in patients with CL/P.

Application of Machine Learning Models for Malware Classification With Real and Synthetic Datasets

Stacking of multiple Machine Learning (ML) classifiers have gained popularity in addressing anomalous data classification along with Deep Learning (DL) algorithms. This study compares traditional ML classifiers, multi-layer stacking ML classifiers, and DL classifiers using an open-source malware dataset-containing equal numbers of benign and malware samples. The results on the realistic dataset indicate that the DL classifier, utilizing a Bidirectional Long Short-Term Memory (BiLSTM) model, outperformed the stacked classifiers with Logistic Regression (LR) and Support Vector Machine (SVM) as Meta learners by 36.78% and 39.69%, respectively, in terms of classification accuracy and performance. The research work was extended to study the impact of Generative Adversarial Network (GAN) based synthetic dataset of relatively smaller size on deep learning models. It was observed that the Deep Learning Multi-Layer Perceptron (DLMLP) Model had relatively superior performance as compared to complex deep learning models like Long Short-Term Memory LSTM and BiLSTM

Transformation and development of simulation-based education and child first aid skills training

Background: Simulation-based medical education is a complex learning methodology in different fields. Exposing children to this teaching method is uncommon as it is designed for adult learning. This study aimed to develop and implement simulation-based education in first aid training of children and investigate the emotions of children in post-simulation scenarios that replicate emergency situations. Methods: This was a phenomenological qualitative research study. The participants attended the modified “Little Doctor” course that aims to train children in first aid and, subsequently, completed simulation scenarios. The children attended focus groups and were asked about their experiences of the course and how they felt during the simulation scenarios. Results: 12 children (Age 8–11 years old) attended the course, and 10 completed the simulation scenarios and focus groups. The major theme derived from was the simulation experience’s effect, which was divided into two subthemes: the emotion caused by—and the behavioral response to—the simulation. The analysis revealed shock and surprise toward the environment of the simulation event and the victim. The behaviors expressed during the simulation scenarios ranged from skill application and empathy to recall and teamwork. Conclusions: Simulation scenarios were successfully implemented during the first-aid training course. Although participants reported mixed feelings regarding the experience, they expressed confidence in their ability to perform real-life skills.

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

AbstractA two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

A Comprehensive Framework for Machine Learning-Based Threat Intelligence in Health Information Systems

This research work provides a comprehensive architecture of Machine Learning based threat intelligence particularly for Health Information System (HIS). The number of cybersecurity threats executed by healthcare companies is even higher since healthcare organizations continue to introduce digitized data into medical data. This work employs complex machine learning techniques from the MIMIC-III Critical Care Database to develop a practical threat identification and mitigation system. In this case, the strategy of analysis involves selection of data, data processing, modeling and real time dangers identification considering both supervised and unsupervised learning. The results reveal that the proposed framework covers high performance indicators such as: accuracy that equals 97.92%, and the level of precision and recall which also equal 90% ROC AUC has reached 0.94. These results demonstrate that the framework can identify and categorise cybersecurity risks in systems of health information on a regular basis. It not only increases threat perception but also makes the system internally valuable for healthcare IT professionals since it contains real-time monitoring and anomaly detection functionality. Therefore, this study stands in support of the ongoing efforts to enhance the security of the healthcare bodies on the use of policies on cybersecurity so as to ensure the protection of individual patient’s information against new forms of threats.

Analog‐Digital Hybridity of Resistive Switching in Ion‐Irradiated BiFeO3 Memristor for Synergistic Neuromorphic Functionality and Artificial Learning

AbstractMemristors‐based neuromorphic devices represent emerging computing architectures to perform complex tasks by outpacing the traditional Von‐Neumann architectures in terms of speed, and energy efficiency. In this work, the resistive switching (RS) behavior of sol‐gel grown and ion‐irradiated BFO films is investigated under electrical stimulus. The Ag/BFO/FTO memristors emulate a combination of digital and analog RS behavior within a single device. The possible mechanism of analog digital hybridity is addressed by considering the formation of the conducting filament by oxygen vacancies, Ag+ ions and Schottky barrier height modulation. The ion‐irradiated BFO samples are analyzed using the Raman, XRD, and XPS studies. To uphold bioinspired synaptic actions, crucial synaptic functionalities like pair‐pulse facilitation and long‐term potentiation/depression are effectively achieved. More intricate synaptic behaviors are also demonstrated such as spike‐time‐dependent plasticity and Pavlovian classical conditioning, which represent the prominent attributes of both learning and forgetting behavior. Additionally, high pattern recognition accuracy (96.1%) is achieved in an artificial neural network simulation by using the synaptic weights of the memristors. This synergistic effect of digital and analog RS in ion‐irradiated BFO can be beneficial for the emulation of complex learning behavior as well as its incorporation into low‐power neuromorphic computing.

Children’s Expressed Emotions During Playful Learning Games

Studies on software tutoring systems for complex learning have shown that confusion has a beneficial relationship with the learning experience and student engagement (Arguel et al., 2017). Causing confusion can prevent boredom while signs of confusion can serve as a signal of genuine learning and as a predecessor for frustration. There is little to no research on the role of confusion in early childhood education and playful learning, as these studies primarily focus on high school and university students during complex learning tasks. Despite that, the field acknowledges that confusion may be caused by inconsistency between information and a student’s internal model referred to as cognitive disequilibrium known from the theory of cognitive development, which was originally theorized based on observational studies on young children (D’Mello and Graesser, 2012). Therefore, there is reason to expect that the virtues of confusion also apply to young children engaging in learning activities, such as playful learning. To investigate the role of confusion in playful learning, we conducted an observational study, in which the behavior and expressed emotions of young children were collected by familiar pedagogues, using a web app, while they engaged with playful learning games designed for kindergartens. The expressed emotions were analyzed using a likelihood metric to determine the likely transitions between emotions (D’Mello and Graesser, 2012). The preliminary results showed that during short play sessions, children express confusion, frustration, and boredom. Furthermore, the observed emotional transitions were matched with previously established models of affect dynamics during complex learning. We argue that games with a learning objective can benefit by purposely confusing the player and how the player’s confusion may be managed to improve the learning experience.

Classification and evaluation of the algorithms for vector bin packing

Heuristics for Vector Bin Packing (VBP) play an important role in modern distributed computing systems and other applications aimed at optimizing the usage of multidimensional resources. In this paper we perform a systematic classification of heuristics for VBP, with the focus on construction heuristics. We bring together existing VBP algorithms and their tuning parameters, and propose new algorithms and new tuning parameters. For a less studied class of multi-bin algorithms, we explore their properties analytically, considering monotonic and anomalous behavior and approximation guarantees. For empirical evaluation, all algorithms are implemented as the Vectorpack library and assessed through extensive experiments. Our findings may serve as the basis for the development of more complex, hybrid algorithms, hyperheuristics and machine learning algorithms. The Vectorpack library can also be adjusted for addressing enhanced VBP problems with additional features, which arise in applications, especially those typical for modern distributed computing systems.

Privacy-preserving and verifiable convolution neural network inference and training in cloud computing

With the rapid development of cloud computing, outsourcing massive data and complex deep learning model to cloud servers (CSs) has become a popular trend, which also brings some security problems. One is that the model stored in the CSs may be corrupted, leading to incorrect inference and training results. The other is that the privacy of outsourced data and model may be compromised. However, existing privacy-preserving and verifiable inference schemes suffer from low detection probability, high communication overhead and substantial computational time. To solve the above problems, we propose a privacy-preserving and verifiable scheme for convolutional neural network inference and training in cloud computing. In our scheme, the model owner generates the authenticators for model parameters before uploading the model to CSs. In the phase of model integrity verification, model owner and user can utilize these authenticators to check model integrity with high detection probability. Furthermore, we design a set of privacy-preserving protocols based on replicated secret sharing for both the inference and training phases, significantly reducing communication overhead and computational time. Through security analysis, we demonstrate that our scheme is secure. Experimental evaluations show that the proposed scheme outperforms existing schemes in privacy-preserving inference and model integrity verification.

Human α-synuclein aggregation activates ferroptosis leading to parvalbumin interneuron degeneration and motor learning impairment.

The accumulation of α-synuclein induces neuronal loss in midbrain nuclei and leads to the disruption of motor circuits, while the pathology of α-synuclein in cortical regions remains elusive. To better characterize cortical synucleinopathy, here we generate a mouse model with the overexpression of human α-synuclein in the primary motor cortex (M1) of mice. A combination of molecular, in vivo recording, and behavioral approaches reveal that cortical expression of human α-synuclein results in the overexcitation of cortical pyramidal neurons (PNs), which are regulated by the decreased inhibitory inputs from parvalbumin-interneurons (PV-INs) to impair complex motor skill learning. Further mechanistic dissections reveal that human α-synuclein aggregation activates ferroptosis, contributing to PV-IN degeneration and motor circuit dysfunction. Taken together, the current study adds more knowledge to the emerging role and pathogenic mechanism of ferroptosis in neurodegenerative diseases.

Service-Learning and Project-Based Learning Methodologies in Higher Music Education: a review of the literature

INTRODUCTION. Recent research highlights the potential of multidimensional and holistic learning in educational transforation, the provision of social services from the university and development of professional identity. This article aims to analyse the relationship between the Service-Learning and Project-Based Learning methodologies in higher music education and the acquisition of transversal competences. METHOD. A review of 29 articles on both methodologies in higher music education, published between 2012 and 2022, was conducted. The articles were selected from WoS, Scopus, and ERIC databases. Four categories were explored: (1) Social interaction, (2) personal growth, (3) academic learning, and (4) challenges related to the implementation and contextualization of the methodologies. RESULTS. Results show that, in the context of higher music education, both methodologies have a potentially positive impact on the development of transversal competencies in students. The review shows benefits in social aspects such as the development of civic responsibility and communication skills among peers and teachers; academic aspects such as adaptation to complex learning environments, mutually beneficial goals and shared authority; and personal development aspects such as flexibility, solidarity and respect. The review also shows that reflective practice contributes to developing students’ openness to constructive criticism, which is perceived as a positive contribution to learning. CONCLUSIONS. The methodologies studied can foster students’ personal growth, the mitigation of their biases in the classroom and the reflective and conscious appreciation of their own learning. To be taken into account are the challenges related to the complexity of adjusting the projects to the academic curriculum and community needs, as well as the large amount of time and effort required for the process. Finally, further research using different search terms and criteria is encouraged to achieve a broader understanding of how music initiatives can be designed and implemented through these methodologies, the projection they can have, and how research is conducted in this field.

Globalisation and Digitalisation: The European Approach to Cutting-edge Educational Practices

The urgency of optimising the development of Ukraine’s education system is driven by the need to preserve intellectual potential and national identity against the backdrop of the priority of integrating into the European system in the context of post-war recovery. The research aims to identify gaps in existing studies on globalisation and digitalisation practices based on successful European experiences and propose a new research direction in immersive technologies. The innovative solutions proposed in the article pertain to immersive technologies integrated into the educational process to enhance material assimilation and create engaging experiences. The advantages of their application are highlighted, including the visualisation of abstract and complex learning concepts, stimulation of active participation in interaction with virtual objects and environments, development of creativity and problem-oriented thinking, and obtaining realistic experience. The study establishes that the implementation of advanced educational practices of the European community and the involvement of long-term international funding in synergy form potential optimal prerequisites for developing the higher education system in the national space through the differentiation of powers between various institutions, autonomy in the organisation of educational process management, and the formation of a competitive environment in the higher education sector.