Paper ID #6195Use of Sustainable and Systematic Plan to Assess Student Learning Outcomesfor Non-Traditional IT StudentsDr. Lifang Shih, Excelsior College Li-Fang Shih received her Ph.D. in curriculum and instruction with specialization in online instruction from the State University of New York at Albany. Dr. Shih is current the Associate Dean for the School of Business and Technology at Excelsior College. Her researches have focused on issues related to quality online instruction, outcome assessments, online communication, and the development of online commu- nity, etc. Her papers have been presented in national
Computing Learning Activities with ScratchAbstractIn this paper we present a case study of 117 Colombian elementary grade students’ performanceand perceptions of a learning activity aiming to promote computational thinking guided by theCollege Board’s CS Principles and Scratch. The lesson plan was designed by the teacher as partof a three-day teacher professional development workshop within an advanced topics course fora master degree in engineering. As part of the workshop, participants were invited to implementtheir designs in their own classrooms and, together with the researchers, conduct classroomaction research. Workshop participants designed their own instruments and gathered data onstudents’ perceptions of the learning module and identified
. ○ Recruiting techniques for more and diverse computing majors. ○ Pedagogical best practices that result in more and diverse computing majors (e.g., pair programming). ○ Teacher success stories. ● Time every day to reflect, plan for action, and share thoughts and experiences. ● Physical movement, especially as the end of the day approaches. ● Both at-workshop and follow-up evaluation of workshop efficacy and follow-up evaluation of participant outcomes, ● Participant compensation out of respect for their interest in improving high school computer science education and recognition of the value of their time.Each of these principles is addressed in the following sections.Organizer recruitment, selection, and
discuss artificial intelligence through computer science usingheuristics. Additionally, the students debate the ethics associated with artificial intelligence andwhen legal rights should apply to artificial beings.Teachers attend a week-long, immersive professional development workshop for Cyber Sciencethe summer prior to teaching the course6. Following the professional development workshopteachers gain access to all curriculum materials including lesson plans, master notes, andsupplemental documents which are found on NICERC’s website. Communication is maintainedthroughout the school year via the website allowing for any questions, concerns, or issues ateacher may experience when teaching the material.Narrative #1 – High School Teachers
progress at a designated level of proficiency impacts motivation positively. Con-structivist theory10, 11 suggests that the exchange of timely feedback can encourage students tomodify their work. Lovett and Greenhouse12 show that receiving feedback and comments on thesteps of learning have significant influence on learning compared to only receiving feedbackfrom the instructor on the performance. Page 23.549.3In the next section, details of the tutorial modules are discussed. The methods section presents awide variety of results from testing these tutorials in a quasi-experimental setting during Fall2012. The discussion section provides our plan
Stavanger—is afaculty-initiated partnership during an international conference. Initial exchange began with avisiting scholar from Norway, followed by significant funding from the Norwegian government tosupport the partnership.The formal collaboration began with a two day intensive “kick-off workshop” at the University ofStavanger, where all faculty participants met face-to-face to discuss the short and long-termgoals of the partnership. This highly successful meeting enabled the faculty partners to buildworking relationships, create an action plan, and establish goals and timelines. Over the courseof the workshop, several challenges were identified, including mismatches in academic calendars,differences in the structure of graduate advising and
background of Prolog, search, agents, knowledge / rulebased systems, planning and natural language processing43. The following topics are coveredfrom the machine learning and computational intelligence part of the course: an overview ofmachine learning, simple learning methods, neural learning and evolutionary computations.Students from the School of Engineering and Computer Science usually take this elective course.There are four assignments for the course, two for the symbolic intelligence and two for machinelearning and computational intelligence. Through these four assignments, students gain hands-on experience in applying these techniques to real-world applications. This course offersessential background and training for the students to start
to help build the pool of IPv6 talent through high quality, certified IPv6 courses.The IPv6 Security course is the second in a series of three IPv6 courses we plan to deliver atECU. In summer 2012, we delivered an IPv6 Fundamental course which met with great success.In summer 2013, we will offer both the IPv6 Fundamentals course and the newly developed IPv6Security course. During the 2013 Summer Semester, we will solicit student feedback on theneed for a third course that will cover “advanced” service provider centric IPv6 topics. We willalso continue to work with the Nephos6 academy to develop academic curriculum tocomplement the courses and the lab assignments.Bibliography1. Brustoloni, C. 2006 Laboratory Experiments for Network
with anintegrated learning management system. The first part of the paper provides a description of thedevelopment process of remote laboratory, while the second part details the LMS that is beingused to facilitate the remote laboratory. In addition, the LMS provides a means of monitoringand analyzing the usage of the facility through different web applications. Effort has been madeto use cutting edge technologies to maximize the benefits of modern technologies throughout thedevelopment process. The lead author is planning to deploy the developed system by offering alaboratory course over the Internet. Once completed, the authors will be able to share theexperience in the future.AcknowledgementThe authors would like to thank the National
this tracker, students were exposed to a system that dynamically builds a 3Dmap of the physical environment. As of this writing, 13th Lab plans on releasing a Unity pluginwithin the next few days. Though there were clearly similarities across all the trackingtechnology, by providing multiple examples of trackers, we were able to have meaningfuleducational discussions about the affordances of each of the trackers, comparing and contrastingbetween them as they were introduced.Two Example ProjectsIn order to provide a better understanding of the kind of projects that were developed, twostudent examples are briefly presented. The projects both use the Vuforia SDK (likely due to theorder that the trackers were introduced as well as online support
leaveswomen feeling like outsiders. This perception is further exacerbated by the lack of role modelsand mentors to help women themselves as part of the computing community. While many of these causes continue to be problematic, this section offered several opportunitiesthat may be available to the field of computing to increase women’s interest, enrollment, andretention in the field. On a systemic level, we have highlighted the need for better mentorship forcurrent female students and faculty to make them want to stay in the field and become rolemodels to future students. Research also suggests that there needs to be more strategicrecruitment plans that combat the stereotypes of computing and encourage women to participateby supporting current
and end users—indicate they are using a wireless mesh protocol for at least some of their wireless field devices,and 20% are only using wireless mesh systems. Over half of the WSN adopters are using energyharvesting for at least a few wireless sensor nodes, and 9% use energy harvesting to power themajority of their wireless field devices.Compared with ON World’s previous survey in 2010, data reliability has dropped to only abouttwo-thirds as much of a concern compared with the previous 2010 survey. Costs, battery life, andstandards confusion are ranked slightly higher as inhibitors in our current survey compared withthe previous survey6. Seventy percent of end users indicate they are planning WSN or additionalapplications.Looking forward