Online learners’ experiences and views towards online courses: A case study of the University of the South Pacific

In higher education, demand for online courses has risen over the years, and higher education institutes (HEIs) are investing heavily in the development and delivery of online courses. As a regional university, the University of the South Pacific (USP) is no exception and has made an incremental shift from face‐to‐face and print methods to blended and fully online methods in course delivery. At USP, significant attention has been given to developing an online learning environment using the Moodle platform, upskilling academics and supporting employees to offer good experience to the learners regardless of their location. An area that needs research, however, is the study experiences of online learners. Are they getting what they expected from online learning? Are online courses intended to satisfy the styles and preferences of their learning? This study tries to answer these two questions by collecting online learners ' opinions and experiences at USP. Data were collected from 75 learners registered in 3 online courses using a questionnaire. Positive student experiences of online learning included greater flexibility, timely feedback and greater opportunities for interaction with academic and peers. Challenges identified included poor internet connectivity and lack of familiarity with the online learning management system and tools for first time online learners. Students highly rated the use of multimedia, online learning materials and online assessments as positive contributors to their learning in online courses. Most of the learners were satisfied with online course design and delivery and reported positive learning experience for the three online courses at USP. However, 20 percent of the learners were not satisfied with their online learning experience. Some aspects such as course navigation and feedback system could be improved and training of first time online learners could further improve student learning experience

Forecasting Housing Prices under Different Submarket Assumptions

This research evaluated forecasting accuracy of hedonic price models based on a number of different submarket assumptions. Using home sale data for the City of Knoxville and vicinities merged with geographic information, we found that forecasting housing prices with submarkets defined using expert knowledge and by school district and combining information conveyed in different modeling strategies are more accurate and efficient than models that are spatially aggregated, or with submarkets defined by statistical clustering techniques. This finding provided useful implications for housing price prediction in an urban setting and surrounding areas in that forecasting models based on expert knowledge of market structure or public school quality and simple model combining techniques may outperform the models using more sophisticated statistical techniques.Clustering, Forecasting, Hedonic price, Housing Submarket, Demand and Price Analysis, C53, R21,

Young Stellar Population of the Bright-Rimmed Clouds BRC 5, BRC 7 and BRC 39

Bright-rimmed clouds (BRCs), illuminated and shaped by nearby OB stars, are potential sites of recent/ongoing star formation. Here we present an optical and infrared photometric study of three BRCs: BRC 5, BRC 7 and BRC 39 to obtain a census of the young stellar population, thereby inferring the star formation scenario, in these regions. In each BRC, the Class I sources are found to be located mostly near the bright rim or inside the cloud, whereas the Class II sources are preferentially outside, with younger sources closer to the rim. This provides strong support to sequential star formation triggered by radiation driven implosion due to the UV radiation. Moreover, each BRC contains a small group of young stars being revealed at its head, as the next-generation stars. In particular, the young stars at the heads of BRC 5 and BRC 7 are found to be intermediate/high mass stars, which, under proper conditions, may themselves trigger further star birth, thereby propagating star formation out to long distances.Comment: 30 pages, 7 Figures, 6 Tables, accepted for publication in Monthly Notices of the Royal Astronomical Societ

MODELING OF EXTRUSION PROCESS USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS

Artificial neural networks are a powerful tool for modeling of extrusion processing of food materials. Wheat flour and wheat– black soybean blend (95:5) were extruded in a single screw Brabender extruder with varying temperature (120 and 140 oC), dry basis moisture content (18 and 20%) and screw speed (156, 168, 180, 192 and 204 rpm). The specific mechanical energy, water absorption index, water solubility index, expansion ratio and sensory characteristics (crispness, hardness, appearance and overall acceptability) were measured. Well expanded products could be obtained from wheat flour as well as the blend of wheat– black soybean. The results showed that artificial neural network (ANN) models performed better than the response surface methodology (RSM) models in describing the extrusion process and characteristics of the extruded product in terms of specific mechanical energy requirement, expansion ratio, water absorption index, water solubility index as well the sensory characteristics. The ANN models were better than RSM models both in case of the individual as well as the pooled data of wheat flour and wheat- black soybean extrusion

Response of drip irrigated Broccoli (Brassica oleracea var. italica) in different irrigation levels and frequencies at field level

Geometric increase in population coupled with rapid urbanization, industrialization and agricultural development are causing increased pressure on global water resources. Agriculture is the largest consumer of fresh water resources, thus the scope of enhancing water productivity in agriculture is taken to be the priority area of research. The right amount and frequency of irrigation is essential for optimum use of limited water resources for crop production as well as management. A field experiment with split plot design was carried out during November to February 2015-16 at PFDC (Precision Farming Development Centre), Water Technology Centre, IARI, New Delhi to study the effect of different irrigation levels and frequencies on Broccoli (Brassica oleracea var. italica) under drip irrigation. The experiment included three levels of irrigation frequencies: N1 (once every day), N2 (once every 2 days) and N3 (once every 3 days) with different irrigation levels of 100, 80 and 60 % of crop evapotranspiration (ETc). Results revealed that drip irrigation frequency significantly (p<0.05) affected the broccoli yield. The maximum yield (24.46±0.18 t/ha) was obtained with 80% of ETc with once in 2 days irrigation followed by 100% of ETc with once in 2 days. Lowest yield (16.53±0.1 t/ha) was obtained at 60% of ETc at once in 3 days irrigation. Overall, it was observed that irrigation on 80% of ETc with once in two days is an appropriate cycle for optimum yield of broccoli