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KeywordHigher Education Advisement Process
Electronic Advisement Record
MetadataShow full item record
AbstractPoster presented at the SUNY CIT 2014 annual conference offering a look at how faculty advisers can electronically record, retrieve, and share information discussed during advising sessions, whether in-person or virtual. The College at Brockport has enhanced its Web Banner Advisees’ List to (1) display imaged documents from each student's paperless record and (2) provide the ability to add pertinent information as a result of advising sessions, email, or other forms of communication.
DescriptionThe Paperless Advising Process is a logical progression resulting from The College at Brockport’s 2012 transition to a paperless student record. In the paperless advising process faculty use the Self-Service Web-based Banner system to access a list of their advisees. This list includes links to imaged documents in the student’s academic record. The web-based Banner Advisee List also provides a note keeping feature so that advisors can record pertinent information discussed during an advising session or even through email and other forms of communication. This information can prove invaluable for confirming prior discussions or addressing issues that sometimes arise from a student (or parent). An accurate advising record is essential to ensuring that every student successfully completes the curriculum. Brockport’s advisors refer to the Paperless Advising process as “automagic” because it provides immediate review of academic and advisement documents previously difficult to access. The process has improved communication between faculty, students and administration. As a result, advisors are more informed and better equipped to advise students in order to ensure positive time-to-degree results and retention rates. Other benefits include increased efficiency in document handling and the ability to provide advisement virtually or from any mobile device.
Showing items related by title, author, creator and subject.
Social Media Emoji Analysis, Correlations and Trust ModelingPreisendorfer, Matthew; Sengupta, Sam; Adviser; White, Joshua; Adviser; Tekeoglu, Ali; Adviser (2018-01-18)Twitter is an ever-growing social-media platform where users post tweets, or small messages, for all of their followers to see and react to. This is old news of course, as the platform first launched over ten years ago. Currently, Twitter handles approximately six thousand new tweets every second, so there is plenty of data to be analyzed. With a character limit of 140 per tweet, emojis are commonly used to express feelings in a tweet without using extra characters that more explaining might use. This is helpful in identifying the mood or state of mind that a person may have been in when writing their tweet. From a computing standpoint, this makes mood analysis much easier. Rather than analyzing a group of words and predicting moods from keywords, we can analyze single (or many) emoji(s), and then match those emojis to commonly expressed emotions and feelings. The objective of this research is to gather large amounts of Twitter data and analyze emojis used to find correlations in societal interactions, and how current events may drive social media interactions and behaviors. By creating topic models for each user and comparing it with the emoji distribution analysis, a trust ”fingerprint” can be created to measure authenticity or genuineness of a given user and/or group of users. The emoji distribution analysis also provides the possibility of demographic predictions. Analysis is not limited to Twitter of course but is used here because the API is free and generally easy to use. This paper aims to prove the validity of emoji analysis as a method of user identification and how their trust models can be used in conjunction with pre-existing models to improve success rates of these models.
Microsoft Office 365 and SharePoint as an Educational PlatformCrosby, Kenneth M.; Kahn, Russell; Thesis Adviser; Schneider, Steven; Second Thesis Adviser (2018-05)This study looks at social constructivist learning theory and andragogy as a means of evaluating Microsoft Office 365® and SharePoint® as a platform for delivering online classes in the basic use of SharePoint to an audience of adult learners (New York State employees). The already wide access within New York State agencies to Office 365 and SharePoint makes it a good candidate for examination. If successful, online learning utilizing Office 365 would help to eliminate the geographical and existing software barriers to delivering occupational training to the more than 130,000 employees. Social constructivist and andragogical learning theories were examined, and key elements identified to establish criteria to aid in evaluating Office 365, and potentially other platforms not specifically geared toward online education. Means of facilitating reflection, metacognition, sociocultural learning, prior and authentic experiences, and generative learning strategies were looked for in addition to support of Malcolm Knowles’ andragogical assumptions. Through prototyping and pilot testing of Office 365’s functionality and features, several affordances were able to be made in support of criteria gathered from the literature review. Areas of strength and weakness as a platform for the delivery of online learning were identified in this process. Its success would vary based on the type of learning. Technical courses and corporate training would be more successful than a soft-skill or creative subject. Out of the box SharePoint provides most of the needed functionality to deliver content but, lacks elements such as a grading system.
High Performance Distributed Big File Cloud StorageShakelli, Anusha; Sengupta, Sam; Adviser; White, Joshua; Reviewer (2016-05-01)Cloud storage services are growing at a fast rate and are emerging in data storage field. These services are used by people for backing up data, sharing file through social networks like Facebook , Zing Me . Users will be able to upload data from computer, mobile or tablet and also download and share them to others. Thus, system load in cloud storage becomes huge. Nowadays, Cloud storage service has become a crucial requirement for many enterprises due to its features like cost saving, performance, security, flexibility. To design an efficient storage engine for cloud based systems, it is always required to deal with requirements like big file processing, lightweight metadata, deduplication, high scalability. Here we suggest a Big file cloud architecture to handle all problems in big file cloud system. Basically, here we propose to build a scalable distributed data cloud storage that supports big file with size up to several terabytes. In cloud storage, system load is usually heavy. Data deduplication to reduce wastage of storage space caused by storing same static data from different users. In order to solve the above problems, a common method used in Cloud storages, is by dividing big file into small blocks, storing them on disks and then dealing them using a metadata system , , , . Current cloud storage services have a complex metadata system. Thereby, the space complexity of the metadata System is O(n) and it is not scalable for big file. In this research, a new big file cloud storage architecture and a better solution to reduce the space complexity of metadata is suggested.