african drylands institute for sustainability

Despite the potential of Learning Management System (LMS) supported asynchronous e-
learning to improve asynchronous e-learning system quality by enhancing learning
effectiveness and academic achievement of HEIs, several challenges are faced in the
process of providing the e-learning mode of study particularly in developing countries. The
study will explore five factors that influence quality of asynchronous e-learning: instructional
design, learner support, contextual factors, student characteristics and instructor
characteristics. The quality factors can be used to evaluate quality so as to monitor and
improve the inputs, processes and outputs of asynchronous e-learning systems. The study
examines existing e-learning literature and then proposes five factors that determine the
quality of e-learning systems.

The purpose of many studies in the field of Information Systems (IS) research is to analyse causal relationship between variables. Structural Equation Modelling (SEM) is a statistical technique for testing and estimating those causal relationship based on statistical data and qualitative causal assumption. Partial Least Square Structural Equation Modelling (PLS-SEM) is the technique that is mostly used in IS research. It has been subject to many reviews either in confirmatory or exploratory settings. However, it has recently emerged that PLS occupies the middle ground of exploratory and confirmatory settings. Thus, this paper intends to propose an updated guideline for the use of PLS-SEM in Information Systems Research in exploratory settings maintaining interpretability. A systematic literature review of 40 empirical and methodological studies published between 2012 and 2016 in the leading journal of the field guide future empirical work.

The purpose of many studies in the field of Information Systems (IS) research is to analyse causal relationship between variables. Structural Equation Modelling (SEM) is a statistical technique for testing and estimating those causal relationship based on statistical data and qualitative causal assumption. Partial Least Square Structural Equation Modelling (PLS-SEM) is the technique that is mostly used in IS research. It has been subject to many reviews either in confirmatory or exploratory settings. However, it has recently emerged that PLS occupies the middle ground of exploratory and confirmatory settings. Thus, this paper intends to propose an updated guideline for the use of PLS-SEM in Information Systems Research in exploratory settings maintaining interpretability. A systematic literature review of 40 empirical and methodological studies published between 2012 and 2016 in the leading journal of the field guide future empirical work.

The purpose of many studies in the field of Information Systems (IS) research is to analyse causal relationship between variables. Structural Equation Modelling (SEM) is a statistical technique for testing and estimating those causal relationship based on statistical data and qualitative causal assumption. Partial Least Square Structural Equation Modelling (PLS-SEM) is the technique that is mostly used in IS research. It has been subject to many reviews either in confirmatory or exploratory settings. However, it has recently emerged that PLS occupies the middle ground of exploratory and confirmatory settings. Thus, this paper intends to propose an updated guideline for the use of PLS-SEM in Information Systems Research in exploratory settings maintaining interpretability. A systematic literature review of 40 empirical and methodological studies published between 2012 and 2016 in the leading journal of the field guide future empirical work.

There is a substantial increase in the use of learning management systems (LMSs) to support e-learning in higher education institutions, particularly in developing countries. This has been done with some measures of success and failure as well. There is evidence from literature that the provision of e-learning faces several quality issues relating to course design, content support, social support, administrative support, course assessment, learner characteristics, instructor characteristics, and institutional factors. It is clear that developing countries still remain behind in the great revolution of e-learning in Higher Education. Accordingly, further investigation into e-learning use in Kenya is required in order to fill in this gap of research, and extend the body of existing literature by highlighting major quality determinants in the application of e-learning for teaching and learning in developing countries. By using a case study of Jomo Kenyatta University of Agriculture and Technology (JKUAT), the study establishes the status of e-learning system quality in Kenya based on these determinants and then concludes with a discussion and recommendation of the constructs and indicators that are required to support qualify teaching and learning practices

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.

Researchers have used many instruments to gather data on the use of Information and
Communication Technology to disseminate information on agricultural inputs towards farmers.
These instruments are in English and based on some theories. The Technology Acceptance Model
(TAM), the Diffusion of Innovation Theory (DOI) and the Unified Theory of Acceptance and Use of
Technology (UTAUT) are the three most popular contemporary technology acceptance models. For
other speaking languages especially French and Bambara, there is a need to translate. The increasing
need for non-English data collection instruments and other survey materials has clearly given recent
figures. Despite the availability of tools for translation, the DOI’s instrument has been barely
translated into French and Bambara. In this paper, we used an adaptation method to translate the
DOI’s instrument into French and Bambara. We produced a method for translating English survey
questionnaire into French and Bambara. The method specifies and describes five steps, which are
prepare, translate, pretest, revise and document.
Keywords: ICT, Agriculture, Translation, French, Bambara

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Intelligent agents have been used in collaborative learning. However, they are rarely used to
facilitate group interactions in collaborative m-learning environments. In view of this, the
paper discusses the use of intelligent agents in facilitating collaborative learning in mobile
learning environments. The paper demonstrates how to design intelligent agents and
integrate them in collaborative mobile learning environments to allow group learners to
improve their levels of group knowledge construction. The design was implemented in a
collaborative mobile learning system running on Modular Object-Oriented Dynamic
Learning Environment (Moodle) platform. The application was used in some experiments to
investigate the effects of those facilitated interactions on the level of group knowledge
construction.

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

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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.

Adoption of computer assisted audit tools and techniques (CAATTs) has become fundamental in many audit methodologies owing to rapid advances in clients' information system usage. Audit standards encourage auditors to adopt CAATTs to improve audit efficiency and effectiveness. However, the pace of adoption has been slow among auditors. We employed a well validated information technology (IT) model, the unifi ed theory of acceptance and use of technology (UTAUT) to model the voluntary adoption of technology in auditing. A survey instrument to collect quantitative data on the model’s predictors, intention to use CAATTs and individual characteristics was used. Data was obtained from 70 auditors of Kenya National Audit Offi ce (KENAO). Results indicate that performance expectancy, effort expectancy, facilitating conditions and professional influence, affect the probability that auditors will adopt and use CAATTs. The model explains 69 percent of the variance of the auditors’ behavioral intention to use CAATTs. Though age, gender and experience are moderating influences to many UTAUT predictors, none had a signiicant effect on intention for auditors. These results suggest UTAUT to be a valid model for studying technology adoption decisions among auditors, but other individual characteristics need to be explored. This paper contributes to literature and research on technology acceptance in general, and is also important to auditing research and practice. To increase CAATTs usage, audit firm’s management needs to develop training programs to increase auditors’ degree of ease and enhance their organizational and computer technical support for CAATTs. Regulators need to make a stronger recommendation; and a more direct regulatory intervention in adoption decisions.

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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.

Web-based learning systems give students the freedom to determine what to study based on each individual learner’s learning goals. These systems support learners in constructing their own knowledge for solving problems at hand. However, in the absence of instructors, learners often need to be supported as they learn in ways that are tailored to suit a specific learner. Adaptive web-based learning systems fit in 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 learner and then to use the attribute values for each learner as stored in the model to determining the kind of learning support that is suitable for each learner. Examples of such attributes are learner knowledge level, learning styles and learner errors committed by learners during learning. There are two important issues about the use of learner models. Firstly, how to initialize the attributes in the learner models and secondly, how to update the attribute values of the learner model as learners interact with the learning system. With regard to initialization of learner models, one of the approaches used is to input into a machine learning algorithm attribute values of learners who are already using the system and who are similar (hence called neighbors) to the learner whose model is being initialized. The algorithm will use these values to predict initial values for the attributes of a new learner. Similarity among learners is often expressed as the distance from one learner 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

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