For the last one decade, research in self-regulated learning (SRL) and educational psychology has proliferated. Researchers and educators have focused on how to support leaners grow their SRL skills on both face-to-face and e-learning environments. In addition, recent studies and meta-analysis have greatly contributed to the domain knowledge on the use of SRL strategies and how they contribute and boost academic performance for learners. However, there is little systematic review on the literature on the techniques and tools used to measure SRL on e-learning platforms. This review sought to outline recent advances and the trends in this area to make it more efficient for researchers to establish the empirical studies and research patterns among different studies in the field of SRL. The findings from this study are concurrent with existing empirical evidence that traditional methods designed for classroom supports are being used for measuring SRL on e-learning environments. Few studies have used learner analytics and educational data mining (EDM) techniques to measure and promote SRL strategies for learners. The paper finally points out the existing gaps with the tools presently used to measure and support SRL on learning management systems and recommends further studies on the areas of EDM which can support SRL.

<p>Earlier forms of distance education were characterized by minimal social interaction like correspondence, television, video and radio. However, the World Wide Web (WWW) and online learning introduced the opportunity for much more social interaction, particularly among learners, and this has been further made possible through social media in Web 2.0. The increased availability of collaborative tools in Web 2.0 has made it possible to have online collaborative learning realized in Higher Learning Institutions (HLIs). However, learners can perceive the online collaborative learning process as challenging and they fail to utilize these collaborative tools effectively. Although a number of challenges have been mentioned in the literature, considerable diversity exists among countries due to diversity in infrastructure support for e-learning and learners’ background. This motivated this study to investigate components of online collaborative learning perceived as challenging by learners in HLIs in Kenya. Using a questionnaire, a survey was conducted in two public universities and two private universities to identify students’ perceived challenges in an online collaborative learning environment. Through purposive sampling the questionnaire was distributed to 210 students using e-mail and 183 students responded. Based on descriptive analysis the following five major challenges were rated as high: lack of feedback from instructors, lack of feedback from peers, lack of time to participate, slow internet connectivity, and low or no participation of other group members. There was also a relationship between the university type (private or public) with the perceived challenges which included: lack of feedback from the instructor (p=0.046) and work load not shared equally among group members (p=0.000). Apart from slow internet connectivity the rest of the challenges were in line with the observed challenges in the literature.These key challenges identified in this study should provide insight to educators on the areas of collaborative learning that should be improved in order to provide access to quality education that supports effective online collaborative learning in HLIs in Kenya.</p>

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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 elearning 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.According to the Organization for Economic Co-operation and Development (OECD), many countries are currently overseeing a massive expansion of higher education through the use of information and communication technologies (ICTs). However, improving quality is one the most significant challenges for Higher Institutions of Education (HEIs), particularly in developing countries. This is as a result of enrollment expansion characterized by a range of weak inputs such as weak academic preparation for incoming students, lack of financial resources, inadequate teaching staff, poor remuneration of staff, and inadequate staff qualifications (Johanson, Richard, & Shafiq, 2011; United States Agency for International Development [USAID], 2014; Aung & Khaing, 2016).Recent studies show that ICT integration in education through e-learning are facing numerous challenges associated with quality. For example, studies in Kenya confirmed that there are quality issues linked to inadequate ICT and e-learning infrastructure, financial constraints, expensive and inadequate Internet bandwidth, lack of operational e-learning policies, lack of technical skills on e-learning and e-content development by teaching staff, inadequate course support, lack of interest and commitment among the teaching staff, and longer amounts of time required to develop e-learning courses (Tarus, Gichoya,& Muumbo, 2015;Makokha & Mutisya , 2016).A related study (Chawinga, 2016) in Malawi on increasing access to university education through elearning observed that the greatest obstacles to e-learning use were: Lack of academic support (77.6%);Delayed end of semester examination results (75.5%); Class too large (74.3%); Delayed feedback from instructors (72.6%); Failure to find relevant information for studies (67%)...

Droughts, with their increasing frequency of occurrence, continue to negatively affect livelihoods and elements at risk. For example, the 2011 in drought in east Africa has caused massive losses document to have cost the Kenyan economy over $12bn. With the foregoing, the demand for ex-ante drought monitoring systems is ever-increasing. The study uses 10 precipitation and vegetation variables that are lagged over 1, 2 and 3-month time-steps to predict drought situations. In the model space search for the most predictive artificial neural network (ANN) model, as opposed to the traditional greedy search for the most predictive variables, we use the General Additive Model (GAM) approach. Together with a set of assumptions, we thereby reduce the cardinality of the space of models. Even though we build a total of 102 GAM models, only 21 have R 2 greater than 0.7 and are thus subjected to the ANN process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The champion ANN model recorded an R 2 of 0.78 in model testing using the out-of-sample data. This illustrates its ability to be a good predictor of drought situations 1-month ahead. Investigated as a classifier, the champion has a modest accuracy of 66% and a multi-class area under the ROC curve (AUROC) of 89.99%

Investigating learner behavior is an increasingly important research topic in online learning. Learning styles and cognitive traits have been the subjects of research in this area. Although learning institutions use Learning Management Systems such as Moodle, Claroline, and Blackboard to facilitate teaching, the platforms do not have features for analyzing data and identifying behavior such as learning styles and cognitive traits. Instead, they only produce certain statistical reports from the daily access records. Even though complex models have been proposed in the literature, most studies are based on a single behavior such as learning styles or cognitive traits but not both. Only a few have investigated a combination of cognition-based theories such as working memory capacity and psychology-based ones such as learning styles. Thus, this study sought to answer the research question of whether it was possible to establish a methodology for the estimation of learning styles and cognitive traits from a learning management system. The study combined the Felder-Silverman Learning Style Model and Cognitive Trait Model as theoretical frameworks to identify behavior in a Learning Management System. This study designed a model for extracting records from Learning Management Systems access records to estimate learning style and cognitive traits. From this, a prototype was developed to estimate the learning style and cognitive traits for each student. The model was evaluated by administering manual tools to students in a classroom environment then comparing the results gathered against those estimated by the model. The results analyzed using Kappa statistics demonstrated the interrater reliability results were moderately in agreement. Taken together, these results suggest that it is possible to estimate the learning styles and cognitive traits of a learner in a Learning Management System. The information generated by the model can be used by tutors to provide a conducive online learning environment where learners with similar behavior ask each other for help. This can reduce the teaching load for online tutors because learners themselves act as a teaching resource. Information on learning styles and cognitive styles can also facilitate online group formation by isolating the individual factors that contribute to team success.

Small-scale cereal farmers dominate agricultural activities in developing countries. These agricultural activities are characterized by low productivity due to lack of agricultural input information. This lack is restrained by the low use of ICTs caused by some factors such as the farmers' perception of ICTs and the ICTs' delivered information quality. We investigated these factors and their effects on ICTs' use by small-scale cereal farmers in developing countries. Sikasso region in Mali was selected as a case. A convenient sample size of 300 cereal farmers was selected. Partial Least Squares Structural Equation Modelling technique was used to analyse the data. The results suggested that the perception i.e. relative advantage, compatibility and simplicity and the delivered information quality were able to explain 77.9% of the variance in the Use of ICTs to access and use agricultural input information. From these results, it is important to take the Relative Advantage, Compatibility, Simplicity and Information Quality as the main factors determining the use of ICTs in developing countries in the cereal production context. A further line of inquiry could be to gather data from other developing countries to validate or find out more factors in such settings.

Abstract-The current Learning Management Systems used in e-learning lack intelligent mechanisms which can be used by an instructor to group learners during an online group task based on the learners" collaboration competence level. In this paper, we discuss a novel approach for grouping students in an online learning group task based on individual learners" collaboration competence level. We demonstrate how it can be applied in a Learning Management System such as Moodle using forum data. To create the collaboration competence levels, two machine learning algorithms for clustering namely Skmeans and Expectation Maximization (EM) were applied to cluster data and generate clusters based on learner"s collaboration competence. We develop an intelligent grouping algorithm which utilizes these machine learning generated clusters to form heterogeneous groups. These groups are automatically made available to the instructor who can proceed to assign them to group tasks. This approach has the advantage of dynamically changing the group membership based on learners" collaboration competence level.