Curriculum Enhancement and Reform to Meet the Needs of Smallholder Farmers in Developing Countries: Survey of Literature

The agricultural education system plays an important role in developing knowledge resources and preparing well-trained individuals and the next generation of labor force that becomes part of the public sector (government), the private sector (entrepreneurs, farm producers, agri-business entities) and the NGOs. An education system that is innovative and responsive to the complex and rapidly changing work environment is critical to ensure the effectiveness of all the institutions that contribute to agricultural development agenda. To make the education system responsive requires developing and implementing curriculum and teaching programs that are relevant to the production needs and employment demands of the agricultural sector. This paper reviews the literature on experiences gained in the development of innovative and demand-driven curriculum to make the postsecondary agricultural education system serve the needs of smallholder farmers in developing countries. The paper reviews the desired characteristics of the formal post-secondary educational system to be effective in fulfilling its role in supplying well-trained and productive work force for the agricultural economy. The current general state of agricultural curriculum in developing countries is reviewed with respect to these desired characteristics. The paper also presents a review of experiences gained in implementing different approaches to develop, enhance and reform agricultural curriculum, identifies constraints, challenges and successful examples of such approaches, and derives recommendations for ways forward.Tertiary education, Curriculum reform, Training, Capacity building, Agricultural development, Developing countries, Agricultural and Food Policy, International Development, Labor and Human Capital, Research and Development/Tech Change/Emerging Technologies, Teaching/Communication/Extension/Profession, O15:Human Resources-Human Development-Income Distribution-Migration, M53:Training, I23: Higher Education and Research Institutions, Q16:R&D-Agricultural Technology-Biofuels-Agricultural Extension Services,

Middle-out approaches to reform of university teaching and learning: Champions striding between the top-down and bottom-up approaches

In recent years, Australian universities have been driven by a diversity of external forces, including funding cuts, massification of higher education, and changing student demographics, to reform their relationship with students and improve teaching and learning, particularly for those studying off-campus or part-time. Many universities have responded to these forces either through formal strategic plans developed top-down by executive staff or through organic developments arising from staff in a bottom-up approach. By contrast, much of Murdoch University's response has been led by a small number of staff who have middle management responsibilities and who have championed the reform of key university functions, largely in spite of current policy or accepted practice. This paper argues that the "middle-out" strategy has both a basis in change management theory and practice, and a number of strengths, including low risk, low cost, and high sustainability. Three linked examples of middle-out change management in teaching and learning at Murdoch University are described and the outcomes analyzed to demonstrate the benefits and pitfalls of this approach

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

OECD reviews of higher education in regional and city development, State of Victoria, Australia

With more than 5.3 million inhabitants Victoria is the second most populous state in Australia. Once a manufacturing economy, Victoria is now transforming itself into a service and innovation-based economy. Currently, the largest sectors are education services and tourism. In terms of social structure, Victoria is characterised by a large migrant population, 24% of population were born overseas and 44% were either born overseas or have a parent who was born overseas. About 70% of the population resides in Melbourne. Victoria faces a number of challenges, ranging from an ageing population and skills shortages to drought and climate change and increased risk of natural disasters. Rapid population growth, 2% annually, has implications for service delivery and uneven development as well as regional disparities. There are barriers to connectivity in terms of transport and infrastructure, and a high degree of inter-institutional competition in tertiary education sector. The business structure in Victoria includes some highly innovative activities such as in biotechnology, but other sectors, especially those with high number of small and medium-sized enterprises, are lagging behind. Most of the larger manufacturing enterprises are externally controlled and there is uncertainty over the long term investments they will make in the state, as well as the place of Victoria in the global production networks

The Role of the Private Sector in Training the Next Generation of Biomedical Scientists

Summarizes the proceedings of a conference to address the unique contribution that private funders can make in ensuring that appropriate and adequate training programs are available for basic and clinical research. Offers conclusions and recommendations

Teaching and learning in virtual worlds: is it worth the effort?

Educators have been quick to spot the enormous potential afforded by virtual worlds for situated and authentic learning, practising tasks with potentially serious consequences in the real world and for bringing geographically dispersed faculty and students together in the same space (Gee, 2007; Johnson and Levine, 2008). Though this potential has largely been realised, it generally isn’t without cost in terms of lack of institutional buy-in, steep learning curves for all participants, and lack of a sound theoretical framework to support learning activities (Campbell, 2009; Cheal, 2007; Kluge & Riley, 2008). This symposium will explore the affordances and issues associated with teaching and learning in virtual worlds, all the time considering the question: is it worth the effort