This document summarizes a study on developing an online academic advising website. It found that (1) traditional academic advising is facing challenges of diminished access and sustainability, (2) an advising website was created called VICandMe that incorporates goals generation, educational roadmaps, and other features, (3) an evaluation found VICandMe was similarly effective to in-person advising based on student perceptions of advising behaviors and satisfaction. However, further development is still needed, including establishing a model dataset, integrating games, and developing the social networking domain to enhance roadmap creation.
Urban big data are increasingly being used in urban GIS research. This presentation, developed for an introductory GIS course, introduces the seven V's of big data: volume, variety, velocity, veracity, value, visualization, and variability. Major sources of urban big data include sensor systems, user-generated content, administrative systems, private sector data, humanities data, and hybrid data. These categories and the seven V's are illustrated with six examples of cutting-edge urban big data research: the Array of Things sensing network, Austin bike data, analysis of London's transport network, scraping Craigslist to analyze rental housing, the Mapping Inequality Project, and the EPA Smart Locations Database. The presentation concludes with a discussion of critiques surrounding the use of big data.
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Sloan C Interdisciplinary Approach Presentation 2009jennife1
American Public University System (APUS) has developed internal processes to ensure the academic quality and integrity of online courses and programs. This session will focus on the process for conducting a system wide interdepartmental program review of academic programs. Quantitative and qualitative metrics will be discussed.
Presentation made during 2011 OMT Division Business Meeting in San Antonio, TX.
2010-2011 OMT Division Officers
Henrich Greve, Past Division Chair
Royston Greenwood, Division Chair
Matt Kraatz, Division Chair Elect
Christine Beckman, Program Chair
Mike Lounsbury, Program Chair Elect
Damon Phillips and Nelson L. Phillips, Third Year Representatives-at-Large
Martin Gargiulo and Bill McEvily, Second Year Representatives-at-Large
Eva Boxembaum and Klaus Weber, First Year Representatives-at-Large
Andrew (Andy) Hoffman, Practice Committee Chair
Joe Labianca, Research Committee Chair
M. Diane Burton, Teaching Committee Chair
Brayden King, Blogging Committee Chair
William (Bill) L. Dougan, Treasurer
Rodney Lacey, Communications Coordinator
Joel Gehman, Division Webmaster
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Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....R R
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Urban big data are increasingly being used in urban GIS research. This presentation, developed for an introductory GIS course, introduces the seven V's of big data: volume, variety, velocity, veracity, value, visualization, and variability. Major sources of urban big data include sensor systems, user-generated content, administrative systems, private sector data, humanities data, and hybrid data. These categories and the seven V's are illustrated with six examples of cutting-edge urban big data research: the Array of Things sensing network, Austin bike data, analysis of London's transport network, scraping Craigslist to analyze rental housing, the Mapping Inequality Project, and the EPA Smart Locations Database. The presentation concludes with a discussion of critiques surrounding the use of big data.
The Pearls and Perils of For-Profit Education (Deborah Riemer)ODLAA
Education is on a fast-paced trajectory towards change, but how do institutions do it amidst an environment steeped in tradition? For-profit institutions, in particular, are bearing the brunt of an establishment fearful of change. One of the outcries frequently heard is that for-profits sacrifice academic quality for enrollment numbers. Maintaining quality, while servicing a growing population of students, is not problematic if appropriate systems are put into place that are focused on student learning, steeped in best practices, and designed for scalability. The vision for this design is inclusive of the whole, integrating a systems approach to quality that is incorporated across all levels of the academic unit ˆ the student level, faculty level, and administrator level ˆ and incorporates learning outcomes assessment within a flexible model of program design.
Sloan C Interdisciplinary Approach Presentation 2009jennife1
American Public University System (APUS) has developed internal processes to ensure the academic quality and integrity of online courses and programs. This session will focus on the process for conducting a system wide interdepartmental program review of academic programs. Quantitative and qualitative metrics will be discussed.
Presentation made during 2011 OMT Division Business Meeting in San Antonio, TX.
2010-2011 OMT Division Officers
Henrich Greve, Past Division Chair
Royston Greenwood, Division Chair
Matt Kraatz, Division Chair Elect
Christine Beckman, Program Chair
Mike Lounsbury, Program Chair Elect
Damon Phillips and Nelson L. Phillips, Third Year Representatives-at-Large
Martin Gargiulo and Bill McEvily, Second Year Representatives-at-Large
Eva Boxembaum and Klaus Weber, First Year Representatives-at-Large
Andrew (Andy) Hoffman, Practice Committee Chair
Joe Labianca, Research Committee Chair
M. Diane Burton, Teaching Committee Chair
Brayden King, Blogging Committee Chair
William (Bill) L. Dougan, Treasurer
Rodney Lacey, Communications Coordinator
Joel Gehman, Division Webmaster
Data-Driven Development (D3) and Evaluation of Enskill EnglishLewisJohnson34
Invited conference presentation at the 2020 International Conference on Artificial Intelligence in Education, based on an article published in the International Journal of Artificial Intelligence in Education.
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....R R
Rezapour R, Diesner J (2017) Classification and Detection of Micro-Level Impact of Issue-Focused Films based on Reviews. Proceedings of 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), Portland, OR.
21st century learners around the world demand personalized curriculum from educators who can easily synthesize global Open Educational Resources (OER) into intentional instruction for students via individualized learning plans and interventions linked to core standards. Discover a cloud-based set of solutions proving these concepts with exceptional student achievement results. Featuring Silverback Mileposts Instructional Improvement System ( www.silverbacklearning.com ), LinkIt Assessment System ( www.linkit.com ) and Gooru search engine for learning ( www.goorulearning.org )
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Presentation made during 2012 OMT Division Business Meeting in Boston, MA.
2011-2012 OMT Division Officers
Royston Greenwood, Past Division Chair
Matt Kraatz, Division Chair
Christine Beckman, Division Chair Elect
Mike Lounsbury, Program Chair
Candace Jones, Program Chair Elect
Martin Gargiulo and Bill McEvily, Third Year Representatives-at-Large
Eva Boxembaum and Klaus Weber, Second Year Representatives-at-Large
Peer Fiss and Martine Haas, First Year Representatives-at-Large
Joe Broshak, Research Committee Chair
David Touve, Teaching Committee Chair
Brayden King, Blogging Committee Chair
William (Bill) L. Dougan, Treasurer
Joel Gehman, Division Webmaster
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8. Advisor Load
1998 2003
400
300
200
100
4 yr Private
2 Yr Private
4 Yr Public 0
2 Yr Public
Retrieved From: The Status of Academic Advising: Findings from the ACT Sixth National Survey (2004)
10. Research Questions
How can an academic advising website be
created with features that incorporate
current academic advising theories to foster
an experience that is equivalent to modern
praxis
11. Research Questions
What are student perceptions of an
academic advising website that assists in the
co-creation of academic goals and the
generation of educational-planning
instruments
12. The Gap
“The technological revolution
has not yet consistently
reached advising systems”
Wes Habley
Founding Member of NACADA
13. The Gap
“Authors on the cutting edge
of advising technology are few
and far between”
Wes Habley
Founding Member of NACADA
21. Peninsula College
Peninsula College
California
19 or Less African-American
20 to 24 American Indian
25 to 29 Asian
30 to 34 Filipino
35 to 39 Hispanic
40 to 49 Pac. Isl.
50 or More Multi-Ethnicity
Unknown White
0% 7.5% 15% 22.5% 30% 0% 10% 20% 30% 40%
NOTE: Data retrieved from CCCCO Data Mart, annual figures for 2009 - 2010
24. Findings - National
Advising on general educational requirements
Records management
Evaluation of transfer credit
100%
75%
50%
25%
1983 1987 1992 1998 2003
25. Findings - Local
• Future Academic Needs
• A Space to Talk
• Forums
• Calendaring
• Study Skills
• Real Time Chat
41. Academic Advising
Inventory Instrument
• Evaluation of Advising Systems
• Theoretically Grounded
• Measure of Perceived Behavioral Styles
• Discrete Answer Choices are Overlaid to a
Continuum of Responses
42. Academic Advising
Inventory Instrument
• Evaluation of Advising Systems
• Theoretically Grounded
• Measure of Perceived Behavioral Styles
• Discrete Answer Choices are Overlaid to a
Continuum of Responses
45. AAI Findings - Part 1
Human VIC
DPA 65.51 64.84
PE 33.26 32.64
ADM 22.66 22.31
SC 9.60 10.20
46. AAI Findings - Part 1
Disaggregated Across Gender and Ethnicity
Female Male Ethnicity*
DPA 63.00 71.25 0.025
PE 31.41 37.47 0.015
*ANOVA p values
47. AAI Findings - Part 1 Goal
• Having an
Educational Goal Human VIC
Influenced DPA and
PE Scores
• Goal Possession DPA
64.20 62.06
(69.90) (72.89)
Resulted in a
Perceived
Prescriptive PE
31.93 30.57
(37.21) (37.52)
Behavioral Style
Goal (No Goal)
48. AAI Findings - Part 3
• Satisfaction Measure
Human VIC
• Four Point Likert-Type
Scale
• No Significant
Differences Observed 2.62 2.69
This work emerged from my class work. After my first full semester in the program, I had worked closely with students to develop a proto-survey of their attitudes and perceptions of school. It was this first mini-project, that an odd result occurred. Nearly all the dimensions of school followed normal distribution, except that of advising - which followed a uniform probability distribution. Very odd indeed.\n\nThis finding was later confirmed by separate results from my college’s institutional researcher. \n
This work emerged from my class work. After my first full semester in the program, I had worked closely with students to develop a proto-survey of their attitudes and perceptions of school. It was this first mini-project, that an odd result occurred. Nearly all the dimensions of school followed normal distribution, except that of advising - which followed a uniform probability distribution. Very odd indeed.\n\nThis finding was later confirmed by separate results from my college’s institutional researcher. \n
This work emerged from my class work. After my first full semester in the program, I had worked closely with students to develop a proto-survey of their attitudes and perceptions of school. It was this first mini-project, that an odd result occurred. Nearly all the dimensions of school followed normal distribution, except that of advising - which followed a uniform probability distribution. Very odd indeed.\n\nThis finding was later confirmed by separate results from my college’s institutional researcher. \n
This work emerged from my class work. After my first full semester in the program, I had worked closely with students to develop a proto-survey of their attitudes and perceptions of school. It was this first mini-project, that an odd result occurred. Nearly all the dimensions of school followed normal distribution, except that of advising - which followed a uniform probability distribution. Very odd indeed.\n\nThis finding was later confirmed by separate results from my college’s institutional researcher. \n
This work emerged from my class work. After my first full semester in the program, I had worked closely with students to develop a proto-survey of their attitudes and perceptions of school. It was this first mini-project, that an odd result occurred. Nearly all the dimensions of school followed normal distribution, except that of advising - which followed a uniform probability distribution. Very odd indeed.\n\nThis finding was later confirmed by separate results from my college’s institutional researcher. \n
This work emerged from my class work. After my first full semester in the program, I had worked closely with students to develop a proto-survey of their attitudes and perceptions of school. It was this first mini-project, that an odd result occurred. Nearly all the dimensions of school followed normal distribution, except that of advising - which followed a uniform probability distribution. Very odd indeed.\n\nThis finding was later confirmed by separate results from my college’s institutional researcher. \n
This work emerged from my class work. After my first full semester in the program, I had worked closely with students to develop a proto-survey of their attitudes and perceptions of school. It was this first mini-project, that an odd result occurred. Nearly all the dimensions of school followed normal distribution, except that of advising - which followed a uniform probability distribution. Very odd indeed.\n\nThis finding was later confirmed by separate results from my college’s institutional researcher. \n
Student services, such as academic advising, have been shown to result in higher graduation rates (Eisen, 2009; Webber, 2010).\n\nThis service is being taxed greatly as measured in advisor load. With a recommended ratio of 300:1 (advisees:advisors), current trends in California put the actual load well past 1000:1 (nacada; UC white paper).\n\nFurthermore, as distance education has been embraced by more institutions - these services will also need to be intelligently transferred to this new domain. \n\nService has some potential opportunities to use technology\n\nOpportunities to help provide these services more efficiency (time wise) \n\n(Efficiency begets access)\n\nNot all 800+ come, under this current model\n\n\n
These numbers have ballooned in the recent budget climate. \n\nUCSD reports that their advisor load has gone from 751 to 827 from Fall 2004 to Fall 2009.\n\n\n
These numbers have ballooned in the recent budget climate. \n\nUCSD reports that their advisor load has gone from 751 to 827 from Fall 2004 to Fall 2009.\n\n\n
These numbers have ballooned in the recent budget climate. \n\nUCSD reports that their advisor load has gone from 751 to 827 from Fall 2004 to Fall 2009.\n\n\n
What is the overall purpose of this study? \nWell, first off - what is academic advising? What are the components of this service? \n\nIs this service sustainable? If not, why not?\n\nCan technological amplification be applied towards making this service sustainable? \n
1) What do people want - that is: how can an academic advising website be created with features that incorporate current theories in order to foster an experience that is equivalent to modern praxis\n\n2) How would such a website be viewed by end-users? - that is: what are student perceptions of an academic advising website that assists in the co-creation of academic goals and the generation of educational-planning instruments \n
1) What do people want - that is: how can an academic advising website be created with features that incorporate current theories in order to foster an experience that is equivalent to modern praxis\n\n2) How would such a website be viewed by end-users? - that is: what are student perceptions of an academic advising website that assists in the co-creation of academic goals and the generation of educational-planning instruments \n
Current GAP in the literature - \n\nThe Gap with which I’m attempting to fill - the use of modern Academic Advising Theories to the online domain.\n\nThis personal email is powerful - toss it in...\n\n\n\n
Current GAP in the literature - \n\nThe Gap with which I’m attempting to fill - the use of modern Academic Advising Theories to the online domain.\n\nThis personal email is powerful - toss it in...\n\n\n\n
Ie: Astin’s Involvement theory (goal - doing)\nTinto’s Theories around Retention through commitment (roadmap)\n
A multi-phase mixed such that each phase built upon the prior work. Where ever possible, experimental design was utilized with control groups of the treatment. \n\nPurpose of the project. The purpose is to determine how this student service can be translated to the online domain. What are the features that are necessary? How will these be perceived by students. \n\n\n
Three phases of my study:\n\n1) Discovery: A direct out cropping from my prior work and discussions with students and advisors. What is Academic Advising today? \n1b) The idea of an intelligent agent that will act as a focal point for the rest of the project and a construct that will make this more readily accessible by the students.\n\n2) VIC and me : A proto AA website that incorporates these components from Discovery.\n2b) The need for explicit Goals Generation and Roadmap Delivery. The reason that this site stands apart from prior attempts. \n\nPrior Attempts - Information is Cheap - Meaning is Expensive. Bringing it back into the forefront\n3) Value Added: So what, how did this treatment compare to the control?... \n\n
Three phases of my study:\n\n1) Discovery: A direct out cropping from my prior work and discussions with students and advisors. What is Academic Advising today? \n1b) The idea of an intelligent agent that will act as a focal point for the rest of the project and a construct that will make this more readily accessible by the students.\n\n2) VIC and me : A proto AA website that incorporates these components from Discovery.\n2b) The need for explicit Goals Generation and Roadmap Delivery. The reason that this site stands apart from prior attempts. \n\nPrior Attempts - Information is Cheap - Meaning is Expensive. Bringing it back into the forefront\n3) Value Added: So what, how did this treatment compare to the control?... \n\n
Three phases of my study:\n\n1) Discovery: A direct out cropping from my prior work and discussions with students and advisors. What is Academic Advising today? \n1b) The idea of an intelligent agent that will act as a focal point for the rest of the project and a construct that will make this more readily accessible by the students.\n\n2) VIC and me : A proto AA website that incorporates these components from Discovery.\n2b) The need for explicit Goals Generation and Roadmap Delivery. The reason that this site stands apart from prior attempts. \n\nPrior Attempts - Information is Cheap - Meaning is Expensive. Bringing it back into the forefront\n3) Value Added: So what, how did this treatment compare to the control?... \n\n
Three phases of my study:\n\n1) Discovery: A direct out cropping from my prior work and discussions with students and advisors. What is Academic Advising today? \n1b) The idea of an intelligent agent that will act as a focal point for the rest of the project and a construct that will make this more readily accessible by the students.\n\n2) VIC and me : A proto AA website that incorporates these components from Discovery.\n2b) The need for explicit Goals Generation and Roadmap Delivery. The reason that this site stands apart from prior attempts. \n\nPrior Attempts - Information is Cheap - Meaning is Expensive. Bringing it back into the forefront\n3) Value Added: So what, how did this treatment compare to the control?... \n\n
Three phases of my study:\n\n1) Discovery: A direct out cropping from my prior work and discussions with students and advisors. What is Academic Advising today? \n1b) The idea of an intelligent agent that will act as a focal point for the rest of the project and a construct that will make this more readily accessible by the students.\n\n2) VIC and me : A proto AA website that incorporates these components from Discovery.\n2b) The need for explicit Goals Generation and Roadmap Delivery. The reason that this site stands apart from prior attempts. \n\nPrior Attempts - Information is Cheap - Meaning is Expensive. Bringing it back into the forefront\n3) Value Added: So what, how did this treatment compare to the control?... \n\n
Student recruitment was limited and not necessary reflective of the institution overall. \n\n
Select demographics comparing the research site with statewide values.\n
Goal of the Discovery Phase: Understanding current praxis\n\nBoth at the national and local levels.\n\nUsed national dataset\n\nUsed an in-house survey instrument\n
Goal of the Discovery Phase: Understanding current praxis\n\nBoth at the national and local levels.\n\nUsed national dataset\n\nUsed an in-house survey instrument\n
NACADA data regarding the roles of an advising system per a national survey of the profession.\n\nIt is interesting that ‘records management’ is on the decline, as most of the existing technologies seem to target this functionality specifically. (LIMS)\n\n
Ironically the idea of real time chat was not considered an important need by students.\n\nYet, universally, this particular functionality was championed by practicing advisors, faculty members, and critical colleagues.\n\n\n
First off - what is academic advising? (A type of third space)\n\nPer NACADA:\n\nNational Academic Advising Association. (2006). NACADA concept of academic advising. Retrieved from http://www.nacada.ksu.edu/Clearinghouse/AdvisingIssues/Concept-Advising.htm\n\nEssententially, this is an instructional event - one between student and advisor; whereby, the two mutually explore pathways towards a goal. This goal is something that is also developed within this third space.\n\n
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\n\n\nThose components that the national and local data suggested were of importance to the target audience - students seeking academic advising assistance \n\n
An adaptive process that uses a combination of user input and prior data to generate a goal(s). \nThis goal is then shuttled to an integration matrix where it is first determined to be an acceptable goal or not. If not, then this information is carried back to the goals engine and the process is refined. If the goal is determined to be acceptable, then the system parses to the roadmap generator.\n\nThe roadmap generator, using user input, goal developed, and target institution begins to align these into an educational roadmap. This roadmap is similar to a formal student educational plan (SEP) and can then be used in a face-to-face advising meeting or other arena for final approval; the roadmap could be used as is, if these more traditional forums are not readily available. \n
Based upon CIP\n\nCognitive Information Processing : A programmatic approach that uses questions that drive one towards a path of thought. \n\n\n
The Co-Creation Phase: Is the path that was created a good fit. The advisee is able to make this determination and modify it.\n\nAn expert system would negotiate between to arrive at the best possible solution set.\n
Using the needs of the advisee (i.e. their individual goal) how can these be further integrated into the pre-existing educational structures (i.e. articulation agreements between institutions)? A roadmap that maximizes ‘flexibility’ (for future change) and minimizes time within said system. \n\nInitially, a multi agent based approach that used independent AI was thought to be best\n\nHowever - \n
There is a near lack of standardization between these systems:\n\nThis made for an impossibility of feeding an expert system meaningful data\n\nWithout a heuristics engine, the idea of co-creation would be greatly hampered\n\nAlso, how would it generate an optimized path through the system?\n
Therefore, A team of recently transferred students were assembled. Using these ‘capable others’ design choices were made:\n\n1) Focus was agreed to what: systems and majors would be tackled.\n\n2) CIP or algorithmic program was used as a goals engine to mimic co-creation using a drill-down approach. The use of human thinking upon the questions tempered the purely prescriptive logic such a system imposes.\n\n3) Educational Roadmaps for this now limited vector space were generated in a game like environment\n\nThis last point is CRITICAL - this may be the linkage between future endeavors and creating this optimized pathway!\n\n\n
Lastly, the role of the researcher.\n\n\n\n
Design choices\n
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Sought to determine the perceived value of the tool created, as it pertained to the dispensing of AA\n\n\n
What are student perceptions of the behavioral style of the academic advising experience that they are currently receiving at PC versus the experience behind VIC?\n\nThe use of a mixed-methods strategy that combines a design of experiment with interview followup.\n\nTherefore, PC can act as a baseline or control with which post-hoc data analysis can then be performed on the VIC dataset. \n
As this study seeks to determine the feasibility of providing academic advising services online and a proto-website was created; this instrument permits the evaluation of this advising system.\n\nIn order to determine comparability or equivalency between this online system, using Peninsula College (PC) as a control made sense.\n\nThe Instrument itself: Talk about Cronbach Alpha (0.78); Theoretically Grounded in the work of Crookston.\n\nHow I created it in Survey Monkey - modifications \n
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Across this instrument’s four scales measuring behavioral style of the advising activity, it is interesting that there was no significant differences between student perceptions between these two “treatments”. \n\nTreatments in this study can be thought of the two advising systems (Human/PC and VIC).\n\n\n
Story is different when the data is disaggregated\n\nDPA and PE Scores for participant perceptions differed based upon their gender and ethnicity for the Human Treatment\n\nThere are no differences observed for VIC participants \n\n\n
Interestingly enough - this is stating that those students that already have a goal, they perceive an academic advising system as one being prescriptive...\n\nFor these two measures: DPA and PE. There is no sig effect on the other two scales: ADM, SC (relatively objective measures)\n\nThis can be exploited by VIC - as a student that has a goal seems to perceive the role of AA as prescriptive; the providing of information. A task that is ideally suited to this format (i.e. a website). Therefore, the choice to have a goals engine is further justified. \n
Part 3 - a Measure of how well the academic advising system is viewed by student participants.\n\nFrom the measures:\n\n1) Overall (ONLY ONE SHOWING!) p 0.45 d = 0.07\n2) Accuracy of Information\n3) Adequacy of notice about deadlines\n4) Availability \n5) Time available during sessions\n\nAll were the same between the two treatments.\n\nFischerion (sp) hypothesis testing used, with p value set at 5%\n
These two treatments are (overall) indistinguishable from one another using this particular instrument.\n\nThis instrument was initially developed in order to evaluate different academic advising systems.\n\n+++\n\nWe can understand these results using a quarter pounder with cheese analogy:\n\nOne of these sandwiches was made by a human, the other by a robot. Neither case warrants a call back home - but what if I told you that in the case of the robotic form, there will never, ever, be spit - and isn’t that somewhat reassuring? \n\nTo receive a service devoid of potential judgement and malice. \n
These two treatments are (overall) indistinguishable from one another using this particular instrument.\n\nThis instrument was initially developed in order to evaluate different academic advising systems.\n\n+++\n\nWe can understand these results using a quarter pounder with cheese analogy:\n\nOne of these sandwiches was made by a human, the other by a robot. Neither case warrants a call back home - but what if I told you that in the case of the robotic form, there will never, ever, be spit - and isn’t that somewhat reassuring? \n\nTo receive a service devoid of potential judgement and malice. \n
Rich data to complement the findings from the AAI survey instrument.\n\nInterview guides used for the two separate interviews. Orientation of these two guides were: I) Goals II) VIC Usage \n \nThemes that emerged from these interviews were:\n\nThe College experience\nExperiences\nThe Doctor’s Visit\nOnline Academic Potentials (Pro and Con)\n\nStudents talked about the difficulty of seeing an advisor, the challenges of carving time, decreases to this service, and the inability of the current online tool to do meaningful work.\n\n\n
In addition to efficiencies/effectiveness relative to current praxis; \n\nThere exists the real potential of increasing the ownership of the planning process - a cornerstone of developmental academic advising \n
Using primarily interview data to make the case for the need for greater efficiency.\n\nThis resources is diminishing - \n\nOnline tools are being experimented with - though these are information management systems\n\nAre not equivalent to the current experience, due to design choices (i.e. not grounded in theory)\n\nHence these systems are not getting work done, and not helping make for a more efficient use of student time.\n\nThese systems do however allow for bean counters to know how much ‘work’ their advisors are performing in a given day - not if something gets accomplished.\n
Time per session is decreasing, as are total numbers of advisors. \n\nOne could state that so are FTES; but the difference in these rates can best be characterized by the Advisor load - which is ballooning\n\nWith a continued cuts to the CCC system, as seen in recent mid-year reductions, all services are in jeopardy - including AA\n\nA meaningful tool, one that better mimics the AA experience would increase those that self-serve and could free up funding. \n\nAdditionally, expenditures are consistent across all 72 districts. \n
Time per session is decreasing, as are total numbers of advisors. \n\nOne could state that so are FTES; but the difference in these rates can best be characterized by the Advisor load - which is ballooning\n\nWith a continued cuts to the CCC system, as seen in recent mid-year reductions, all services are in jeopardy - including AA\n\nA meaningful tool, one that better mimics the AA experience would increase those that self-serve and could free up funding. \n\nAdditionally, expenditures are consistent across all 72 districts. \n
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Toolset forged in theory / practice to be used by practitioners and students alike\n\nShifting greater control to students - greater ownership of the planning process (cornerstone to developmental academic advising practices)\n\nFreeing up Resources by allowing self regulation - shifting or allocation of dwindling advisor resources to be spent on those that are in need.\n
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Components or tools that would need to be individually engineered to allow for VIC 2.0\n\nAnd how to get these\n
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A great example of a POLICY Heavy endeavor. \n\nThe liberation of a common core of real life student pathways to allow researchers from all fields of study to work with as they see fit.\n\nThe Netflix example - \n\nStandard datasets to permit outside research teams the chance to compete for a better tool to picking the ‘right‘ movie from their catalogue. \n\nApplying this same idea to expand the base of tools to find optimized routes. \n
Begin the process of having complete student transfer paths made available via this portal.\n\n23andMe - problem; dataset would be BIG\n\nTools to work with this would need to be re-thought out (eg: how to handle lack of standardization across institutions) - something like inodes in UNIX???\n\nPrivacy issues would need to be worked out. \n
But maybe the real answer to this question of how best to locate optimized pathways through a system could rely instead upon the power of crowd-sourcing this.\n\nComputers are good for some types of problems (np) but not all (o^p) - and human nodes could be better suited to do this type of work.\n\nmaybe mention Amazon (EC2) Vs. Mechanical Turk services...\n\nDistributive models have been applied in the past. SETI at home...\n\nThese have matured and now incorporate human nodes using existing gaming engines to extract work units\n\n\n
But maybe the real answer to this question of how best to locate optimized pathways through a system could rely instead upon the power of crowd-sourcing this.\n\nComputers are good for some types of problems (np) but not all (o^p) - and human nodes could be better suited to do this type of work.\n\nmaybe mention Amazon (EC2) Vs. Mechanical Turk services...\n\nDistributive models have been applied in the past. SETI at home...\n\nThese have matured and now incorporate human nodes using existing gaming engines to extract work units\n\n\n
But maybe the real answer to this question of how best to locate optimized pathways through a system could rely instead upon the power of crowd-sourcing this.\n\nComputers are good for some types of problems (np) but not all (o^p) - and human nodes could be better suited to do this type of work.\n\nmaybe mention Amazon (EC2) Vs. Mechanical Turk services...\n\nDistributive models have been applied in the past. SETI at home...\n\nThese have matured and now incorporate human nodes using existing gaming engines to extract work units\n\n\n
But maybe the real answer to this question of how best to locate optimized pathways through a system could rely instead upon the power of crowd-sourcing this.\n\nComputers are good for some types of problems (np) but not all (o^p) - and human nodes could be better suited to do this type of work.\n\nmaybe mention Amazon (EC2) Vs. Mechanical Turk services...\n\nDistributive models have been applied in the past. SETI at home...\n\nThese have matured and now incorporate human nodes using existing gaming engines to extract work units\n\n\n
But maybe the real answer to this question of how best to locate optimized pathways through a system could rely instead upon the power of crowd-sourcing this.\n\nComputers are good for some types of problems (np) but not all (o^p) - and human nodes could be better suited to do this type of work.\n\nmaybe mention Amazon (EC2) Vs. Mechanical Turk services...\n\nDistributive models have been applied in the past. SETI at home...\n\nThese have matured and now incorporate human nodes using existing gaming engines to extract work units\n\n\n
But maybe the real answer to this question of how best to locate optimized pathways through a system could rely instead upon the power of crowd-sourcing this.\n\nComputers are good for some types of problems (np) but not all (o^p) - and human nodes could be better suited to do this type of work.\n\nmaybe mention Amazon (EC2) Vs. Mechanical Turk services...\n\nDistributive models have been applied in the past. SETI at home...\n\nThese have matured and now incorporate human nodes using existing gaming engines to extract work units\n\n\n
This could be a means of connecting recent alumni with current students - a model that is already being explored within the context of student loans (i.e. SoFi.com) \n
Figuring out how to optimize a path through the educational system is really the big hurdle, as this will be the product that students want the most.\n\nIntegration of their inputs can be accommodated using existing strategies (heuristic engines) but having a rapid means of ‘recomputing’ a pathway would present the adaptiveness necessary for adoption.\n\nAssuming that this could be accomplished - what’s next? \n\nthat’s where I thought about how could one leverage modern tools to re-introduce the social experience of course selection\n\n
Design is a plan for arranging elements in such a way as best to accomplish a particular purpose.\n\nCharles Eames \n— Charles Eames\n\n\nCharles Eames\n\n\n
Design is a plan for arranging elements in such a way as best to accomplish a particular purpose.\n\nCharles Eames \n— Charles Eames\n\n\nCharles Eames\n\n\n
Design is a plan for arranging elements in such a way as best to accomplish a particular purpose.\n\nCharles Eames \n— Charles Eames\n\n\nCharles Eames\n\n\n
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The Shaw quote - and that technologies to be applied authentically, ought to have a humanizing effect. Providing us connections. \n\nSome recall going to the gym to pick classes - this was a social experience.\n\nNow we have students waking up at midnight in there bedrooms to make potentially life altering decisions\n\nWhy not apply technology in service of rather than in spite of student success\n\n\n
\n\nBringing back the social experience in course selection.\n\nWhile at the same time close integration with intelligent mechanisms for course selection, tailored to the individual’s goals.\n