Recent years have witnessed an increased interest in using social media/social learning with online courses. Many students value the social aspects of their university education and hesitate to take online courses thinking them to be devoid of socialization. On the other hand questions have arisen with regard to privacy and instruction into the social dimension of students. This study examined the following questions: 1) Does social media and social learning promotes educational opportunities for improved student understanding? 2) Do students view social media as an asset to courses -does it enhance understanding and keep student engaged? 3) What do university students see as problems with the use of social media? An online survey was developed consisting of 34 questions relating to the demographics of the sampled 1376 university students and questions relating to the previously mentioned questions. Percentages, t-tests, and correlations were analyzed. Results found subtle nuances and interesting trends (as well as further questions), but overall the results indicated positive attitudes and confirm the value of using social media/social learning in online courses. The findings of the study also suggest implications for teaching improvement with online courses at the university level.

According to Social Cognitive Theory (Bandura, 1997), nutritional risk would be predicted by perceptions of nutrition efficacy, which in turn would be predicted from four sources: modeling, verbal support, physiological conditions, and nutrition habits. In telephone interviews with 154 rural elderly adults (44 men, 110 women; mean age = 74.4 years), nutritional risk was measured with Nutritional Risk Index (NRI), Nutritional Screening Initiative (NSI), and seven-high-risk-nutrients consumed less than 50 percent of RDA (Diet Plus Analysis). Nutrition-efficacy was measured with a new twenty-five-item scale of Perceived Nutrition Barriers (PNB). Sources of nutrition-efficacy included: mealtime modeling--shared meals, household size; verbal support--people talk to each day, hours talked, number of confidants; physiological conditions that may affect nutrition--age, body mass, medications, disability, negative affect, and nutrition habits--daily food variety, use of meal services. Path analysis was performed with each measure of nutritional risk (NRI, NSI, 7-high-risk-nutrients) as a criterion variable, nutrition-efficacy (PNB) as a possible mediating variable, and sources of nutrition-efficacy as predictor variables. Social Cognitive variables accounted for 58 percent of variance in NRI, 49 percent of variance in NSI, and 29 percent of variance in seven-high-risk-nutrients. Nutritional risk was directly predicted by large households, few shared meals, few confidants, high body mass, many medications, and few daily foods; it was indirectly predicted (via PNB) by high levels of negative affect. Perceived Nutrition Barriers (PNB) most often mentioned were food cost, eating alone, food tastelessness, transportation to the store, and chewing difficulty.

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Perception of learning needs of older adults is examined on a community-wide basis. Two hundred seventy-five service providers in a rural location with a high concentration of older people (age 60+) were queried about types of needs, obstacles to learning, and program development. While it was found that instrumental needs are generally recognized among service providers, perception of those needs and associated aspects of learning are based on occupational orientation. Age of the service provider and number of contact hours were not related to perceived needs and problems of learning. The findings are discussed in terms of an expanding context of community involvement in providing educational services to the elderly.

Ninety-six low-soeioeconomic-status students in Grades 5, 8, and 11 were presented two classification tasks related to mathematics. Task 1 required students to state how geometric forms presented successively were alike and different. The bases on which students indicated likenesses and differences were classified as perceptible, attribute, nominal, or fiat. Task 2 was a free-sorting exercise in which the students arranged 26 geometric forms successively into seven groups. Unlike middle-and high-socioeconomic-status students, the low-socioeconomicstatus students classified significantly more on the basis of perceptible likenesses and differences among concept examples than on the more mature bases of the defining attributes and the names of concepts. Low socioeconomic status and lack of instruction were related to the patterns of conceptual development.