The Impact of Adaptive Learning in Principles of Microeconomics

Abstract The spread of Covid-19, which forced almost all learning to move to online in March, 2020, abruptly increased the number of undergraduates taking at least one online course by approximately 177% between the fall of 2019 and the spring of 2020 (Koksal, 2020; Carey, 2020; National Center for Education Statistics, 2020). Even without the Covid-19 disruption, online education has become increasing prevalent due to the decreasing allocation of resources to higher education and the pressure on college administrators to make a college education effective, affordable, and accessible for more students. Originally online instruction differed from in-class instruction only be the method of delivery of the material, viewing a lecture online versus being present in a live classroom lecture. Although there have been many studies on the effectiveness of traditional online instruction over the last several decades, there have been fewer studies on the efficacy of the relatively new adaptive learning courseware. This initial study found that adaptive learning had a consistently positive and statistically significant impact on all principle of microeconomics students in the study, regardless of aptitude, ethnicity, and gender. However, students with high aptitudes appeared to benefit more from adaptive learning than their peers

Network slice allocation for 5G V2X networks: A case study from framework to implementation and performance assessment

Empowered by the capabilities provided by fifth generation (5G) mobile communication systems, vehicle-to-everything (V2X) communication is heading from concept to reality. Given the nature of high-mobility and high-density for vehicle transportation, how to satisfy the stringent and divergent requirements for V2X communications such as ultra-low latency and ultra-high reliable connectivity appears as an unprecedented challenging task for network operators. As an enabler to tackle this problem, network slicing provides a power tool for supporting V2X communications over 5G networks. In this paper, we propose a network resource allocation framework which deals with slice allocation considering the coexistence of V2X communications with multiple other types of services. The framework is implemented in Python and we evaluate the performance of our framework based on real-life network deployment datasets from a 5G operator. Through extensive simulations, we explore the benefits brought by network slicing in terms of achieved data rates for V2X, blocking probability, and handover ratio through different combinations of traffic types. We also reveal the importance of proper resource splitting for slicing among V2X and other types of services when network traffic load in an area of interest and quality of service of end users are taken into account.publishedVersionPaid open acces

Agile gravitational search algorithm for cyber-physical path-loss modelling in 5G connected autonomous vehicular network

Based on the characteristics of the 5 G standard defined in Release 17 by 3GPP and that of the emerging Beyond 5 G (or the so-called 6 G) network, cyber-physical systems (CPSs) used in smart transport network infrastructures, such as connected autonomous vehicles (CAV), will significantly depend on the cellular networks. The 5 G and Beyond 5 G (or 6 G) will operate over millimetre-wave (mmWave) bands. These network standards require suitable path loss (PL) models to guarantee effective communication over the network standards of CAV. The existing PL models suffer heavy signal losses and interferences at mmWave bands and may not be suitable for cyber-physical (CP) signal propagation. This paper develops an Agile Gravitational Search Algorithm (AGSA) that mitigates the PL and signal interference problems in the 5G–NR network for CAV. On top of that, a modified Okumura-Hata model (OHM) suitable for deployment in CP terrestrial mobile networks is derived for the CAV-CPS application. These models are tested on the real-world 5 G infrastructure. Results from the simulated models are compared with measured data for the modified, enhanced model and four other existing models. The comparative evaluation shows that the modified OHM and AGSA performed better than existing OHM, COST, and ECC-33 models by 90%. Also, the modified OHM demonstrated reduced signal interference compared to the existing models. In terms of optimisation validation, the AGSA scheme outperforms the Genetic algorithm, Particle Swarm Optimisation, and OHM models by at least 57.43%. On top of that, the enhanced AGSA outperformed existing PL (i.e., Okumura, Egli, Ericson 999, and ECC-33 models) by at least 67%, thus presenting the potential for efficient service provisioning in 5G-NR driverless car applications