Ghadah Aldabbagh

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.

Distributed dynamic load balancing in a heterogeneous network using LTE and TV white spaces

With advances in technology, network operators may need to set up a dynamic spectrum access overlay in heterogeneous networks (HetNets) to increase network coverage, spectrum efficiency, and the capacity of these networks. Uses of TV white space (TVWS) and long term evolution (LTE) are the combination of a new research direction to meet the increasing user demands in the domain of wireless cellular networks. Without the consideration of traffic flow, a network may operate with serious congestion problems that degrade the system performance. Congestion problems can be resolved by either reducing traffic flow or increasing the bandwidth provision. This paper has proposed Distributed dynamic load balancing (DDLB) cellular-based TVWS and LTE technique, such that a cellular-based device can operate on both TVWS and LTE by simply switching its frequency of operation when necessary. The objective of this paper is to resolve the congestion problems in a HetNet through dynamically constructing new clusters to increase the system bandwidth. The simulation results show that the proposed technique solved the bottleneck problem, reduced transmission control overhead and power consumption, and increased the average throughput and load balancing index.

QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces

Wireless networks have resource limitations; in a dense area, cellular spectrum resources are insufficient and affect the system performance. A Long Term Evolution (LTE) network aims to serve heterogeneous users with different QoS requirements. Traditional approaches need new infrastructure and degrade performance of delay sensitive applications, which may result in users with minimum rate requirements being in high blocking probability. To utilize wireless resources efficiently, users want to access the same medium to connect with the same multicast group and be overhauled at the same time. In this paper, a new technique is proposed for operator-controlled called the QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces (QTHN) to improve QoS for Constant Bit Rate (CBR) and Best Effort (BE) users. The proposed QTHN converts the whole dense wireless network into hexagonal clusters via two layer network communication. In a cluster, one node is selected as a cluster head and all other nodes act as slaves. Within a cluster, a cluster head acts as an access point known as a Hotspot (H), which is further connected to the Base-station (BS). The proposed QTHN aims to improve QoS within heterogeneous wireless network using LTE and unused White Spaces in a wireless dense area. Simulation results show that the proposed QTHN reduced the numbers of blocked users and improved network utility.

A type-2 fuzzy logic recommendation system for adaptive teaching

E-learning platforms facilitate the interaction between students and instructors while mitigating temporal or spatial constraints. Nevertheless, such platforms require measuring the degree of students’ engagement with the delivered course content and teaching style. Such information is highly valuable for evaluating the quality of the teaching and altering the teaching delivery style in massively crowded online learning platforms. When the number of learners is high, it is essential to attain overall engagement and feedback, yet doing so is highly challenging due to the high levels of uncertainties related to students and the learning context. To handle these uncertainties more robustly, we present a method based on type-2 fuzzy logic utilizing visual RGB-D features, including head pose direction and facial expressions captured from Kinect v2, a low-cost but robust 3D camera, to measure the engagement degree of students in both remote and on-site education. This system augments another self-learning type-2 fuzzy logic system that helps teachers with recommendations of how to adaptively vary their teaching methods to suit the level of students and enhance their instruction delivery. This proposed dynamic e-learning environment integrates both on-site and distance students as well as teachers who instruct both groups of students. The rules are learned from the students’ and teachers’ learning/teaching behaviors, and the system is continuously updated to give the teacher the ability to adapt the delivery approach to varied learners’ engagement levels. The efficiency of the proposed system has been tested through various real-world experiments in the University of Essex iClassroom among a group of thirty students and six teachers. These experiments demonstrate the capabilities—compared to type-1 fuzzy systems and non-adaptive systems—of the proposed interval type-2 fuzzy logic-based system to handle the uncertainties and improve average learners’ motivations to engage during learning.

Dynamic Clustering Protocol for coordinated tethering over cellular networks

Abstract Tethering has been recently proposed as an efficient solution for the increase in number of mobile users and the requested bandwidth in cellular networks. The idea is to group the mobile nodes into clusters, each containing slaves served by a hotspot. To save licensed spectrum, the hotspot–slave link can use other frequency channels like the newly vacated TV white space (TVWS) bands. A recent work has proposed an iterative clustering algorithm for cellular networks. Despite its computational complexity and signaling overhead, this algorithm did not handle the nodes׳ mobility inside the cell neither the change in their required data rate. In this work, we propose a new dynamic protocol for clustering the nodes taking into account the possible changes occurring in a cellular network. Specifically, the Dynamic Clustering Protocol (DCP) adapts the network configuration with the variable mobiles׳ requirements and the different network events. This will reduce the needed time and signaling and offers better service quality for the clustered users. After presenting the various network events, the handover scenarios and signaling for the Dynamic Clustering Protocol, the performance of the proposed protocol is studied. This was accomplished by modeling different network scenarios and computing the required number of handovers as a function of user mobility, available network resources and data rate requirements for a given clustered nodes configuration.

Coordinated Tethering Over White Spaces

Coordinated tethering over a white-space spectrum is investigated herein to increase mobile broadband spectrum efficiency in densely populated areas. This paper proposes an algorithm for operator-controlled tethering over white spaces. The proposed approach does not add to the existing infrastructure but instead allows the individual nodes to act as “hotspots” and to tether data to and from other nodes. The proposed algorithm iteratively clusters the nodes into hotspots and slaves and allocates resources to maximize spectrum utility. The proposed methods dynamic characteristics allow cellular systems to hierarchically evolve in dense areas as necessary. A signaling framework for node-to-node and base-station-to-node communication that enables such operator-controlled tethering is also presented. Simulation results show that given a fixed amount of network resources, the proposed algorithm can significantly increase the number of supported users.

Users-centric adaptive learning system based on interval type-2 fuzzy logic for massively crowded E-learning platforms

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

Spatial Reuse in Dense Wireless Areas: A Cross-Layer Optimization Approach via ADMM

This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their distance from the destination and creates a structured multi-hop configuration in which each group can relay its neighbors data. The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.

A zSlices-based general type-2 fuzzy logic system for users-centric adaptive learning in large-scale e-learning platforms

Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners’ interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students’ needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students’ preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system’s efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system’s capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems.

Hybrid Clustering Scheme for Relaying in Multi-Cell LTE High User Density Networks

LTE usage is rapidly increasing with the increased demand for high data rate services. LTE cells with high load would be insufficient to handle all users’ traffic. This may cause blocking for some users or degrade the quality for others. Several techniques are adopted to increase the capacity of LTE cells. This paper will address specifically the use of some users as relays to others in order to increase the capacity of a specific cell. The result is a two-hop topology network with some users connected directly to the base station (BS) and others using some of the already connected users as relays to access the BS. Different techniques could be used to configure the users in such a topology. The paper proposes a new algorithm for relay selection in a multi-cell scenario based on K-means and selection strategy.