african drylands institute for sustainability

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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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