Students entering medical college generally show vast diversity in their school education. It becomes the responsibility of teachers to motivate students and meet the needs of all diversities. One such measure is teaching students in their own preferred learning style. The present study was aimed to incorporate a learning style-based teaching-learning program for medical students and to reveal its significance and utility. Learning styles of students were assessed online using the visual-auditory-kinesthetic (VAK) learning style self-assessment questionnaire. When respiratory physiology was taught, students were divided into three groups, namely, visual (n = 34), auditory (n = 44), and kinesthetic (n = 28), based on their learning style. A fourth group (the traditional group; n = 40) was formed by choosing students randomly from the above three groups. Visual, auditory, and kinesthetic groups were taught following the appropriate teaching-learning strategies. The traditional group was taught via the routine didactic lecture method. The effectiveness of this intervention was evaluated by a pretest and two posttests, posttest 1 immediately after the intervention and posttest 2 after a month. In posttest 1, one-way ANOVA showed a significant statistical difference (P=0.005). Post hoc analysis showed significance between the kinesthetic group and traditional group (P=0.002). One-way ANOVA showed a significant difference in posttest 2 scores (P < 0.0001). Post hoc analysis showed significance between the three learning style-based groups compared with the traditional group [visual vs. traditional groups (p=0.002), auditory vs. traditional groups (p=0.03), and Kinesthetic vs. traditional groups (p=0.001)]. This study emphasizes that teaching methods tailored to students' style of learning definitely improve their understanding, performance, and retrieval of the subject.
<span lang="EN-US">Cloud computing is </span><span lang="EN-AU">the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet</span><span lang="EN-US">.It is a</span><span lang="EN-AU"> broad and deep platform that helps customers builds sophisticated, scalable applications.</span><span lang="EN-US"> To get the full benefits, research on a wide range of topics is needed. While resource over-provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning.</span>
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