Prof. Oboko Robert Obwocha

The focus of this study was to review and evaluate the effectiveness of gamification within e-learning platforms. The study deployed systematic literature review methodology to evaluate how effective gamification has been used within e-learning platforms. The study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Approach (PRISMA), starting with 366 articles, shifting to a final 34 articles for consideration. It was established that gamification positively influences and enhances learning within the e-learning platform. Therefore, the study recommends policy makers, designers, and implementers of e-learning platforms to consider incorporating gamification elements in order to increase user motivation and engagement for enhanced learning.

The purpose of the present study was to identify the influencing factors of asynchronous
elearning system quality particular in developing countries via a review of current literature
and a qualitative pre-study conducted at Jomo Kenyatta University of Agriculture and
Technology (JKUAT). Despite the perceived benefits of these systems to overcome
challenges facing education sector in the region, studies show that the majority of them have
not been successful. After a thorough review of existing literature on developing countries
and a qualitative pre-study conducted at JKUAT University, the study identified the factors
that influence quality of e-learning systems as: Course Design, Content support, social
support, and Student Characteristics, Instructor Characteristics, Technician Characteristics,
Course Assessment and Institutional factors.

Learning Management Systems such as Modular Object-Oriented Dynamic Learning
Environment (Moodle) only supports random group assignment or instructor based
assignment method. However, with the understanding that random assignment method only
increases the likelihood of heterogeneity in the group, while instructor based method
involves the instructors and it is not dynamic, there is need to develop a group formation
mechanism which can guarantee heterogeneity based on learner's collaboration
competence level, has dynamism in grouping students and has less instructor involvement.
In view of this, this paper discusses how to extend Moodle grouping functionality in
discussion forums using an intelligent grouping algorithm which has the capability to mine
discussion forum data in Moodle and cluster students to different clusters based

Scaffolds are a good method of implementing self-regulated learning. Use of prior knowledge makes the
learner to understand a topic better. Learner adaptation enables a learner to be presented with content that
matches his/her level of understanding.
The main aim of this project is to use the adaptive scaffolds in form of concept maps in web-based elearning
systems to play the role of learner guide. The learner creates a concept map from prior knowledge
to show how he/she understands a certain domain of knowledge. The concept map takes into account the
knowledge of the learner in that topic, and uses it to adapt to the user level. This is done by integrated
evaluation where the learner is presented with a concept map that matches his level of understanding as
he/she draws the concept map. The scaffolding and the adaptation are implemented using production rules.
Categories and Subject Descriptions: H.5.2 [Information Interfaces and Presentation]: User Interfaces –
User Centered Design; H.5.4 [Information Interfaces and Presentation]: Hypertext/Hypermedia –
Navigation, User issues; I.2.6 [Artificial Intelligence]: Learning – Concept learning; Induction; K.3.1
[Computers and Education]: Computer Uses in Education – Distance Learning, Computer Assisted
Instruction (CAI); J.4 [Social and Behavioral sciences]: Psychology
General Terms: Algorithms, Human Factors, Experimentation, Measurement, Performance
Additional Key Words: Scaffold, adaptation, prior knowledge, learner evaluation, concept maps,
cognition, adaptive e-learning systems, adaptive scaffolds, integrated assessment.

Web-based learning systems give students the freedom to determine what to study based on each individual student’s learning goals. These systems support students in constructing their own knowledge for solving problems at hand. However, in the absence of instructors, students often need to be supported as they learn in ways that are tailored to suit a specific student. Adaptive web-based learning systems are suited to such situations. In order for an adaptive learning system to be able to provide learning support, it needs to build a model of each individual student and then to use the attribute values for each student as stored in the student model to determining the kind of learning support that is suitable for each student. Examples of such attributes are student knowledge level, learning styles, student errors committed during learning, the student’s program of study, gender and number of programming languages learned by the student of programming. There are two important issues about the use of student models. Firstly, how to initialize the attributes in the student models and secondly, how to update the attribute values of the student model as students interact with the learning system. With regard to initialization of student models, one of the approaches used is to input into a machine learning algorithm attribute values of students who are already using the system and who are similar (hence called neighbors) to the student whose model is being initialized. The algorithm will use these values to predict initial values for the attributes of a new student. Similarity among students is often expressed as the distance from one student to another. This distance is often determined using a heterogeneous function of Euclidean and Overlap measures (HOEM). This paper reports the results of an investigation on how HOEM compares to two different variations of Value Difference Metric (VDM) combined with the Euclidean measure (HVDM) using different numbers of neighbors. An adaptive web-based learning system teaching object oriented programming was used. HOEM was found to be more accurate than the two variations of HVDM. Categories and Subject Descriptions: H.5.2 [Information Interfaces and Presentation]: User Interfaces – User Centered Design; H.5.4 [Information Interfaces and Presentation]: Hypertext/Hypermedia-Navigation, User issues; I.2.6 [Artificial Intelligence]: Learning – Concept learning; Induction; K.3.1 [Computers and Education]: Computer Uses in Education – Distance Learning, Computer Assisted Instruction (CAI) General Terms: Algorithms, Human Factors, Experimentation, Measurement Additional Key Words: Learner modeling, initialization, web-based learning, nearest neighbors, overlap measure, knowledge level, object oriented programming, value difference metric.

This is a Book Chapter in ICCIR series that discuses research work in the areas of Computer Science, Computer Engineering, Information Systems, Information Technology, Software Engineering and Networking. Some of the areas discussed include: Software Usability; Game Theoretic Multi-agent Systems; Dynamic Resource Allocation; Bootstrapping Machine Translation; Exploring the Implementation of Blended Learning; System Dynamics Modeling in Healthcare; Data Security Lapses in Developed Societies