A Prototype for Educational Planning Using Course Constraints to Simulate Student Populations
Distance learning universities usually afford their students the flexibility to advance their studies at their own pace. This can lead to a considerable fluctuation of student populations within a program's courses, possibly affecting the academic viability of a program as well as the related r...
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| creator | Hadzilacos, T Kalles, D Koumanakos, D Mitsionis, V |
| description | Distance learning universities usually afford their students the flexibility
to advance their studies at their own pace. This can lead to a considerable
fluctuation of student populations within a program's courses, possibly
affecting the academic viability of a program as well as the related required
resources. Providing a method that estimates this population could be of
substantial help to university management and academic personnel. We describe
how to use course precedence constraints to calculate alternative tuition paths
and then use Markov models to estimate future populations. In doing so, we
identify key issues of a large scale potential deployment. |
| doi_str_mv | 10.48550/arxiv.cs/0701174 |
| format | Article |
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to advance their studies at their own pace. This can lead to a considerable
fluctuation of student populations within a program's courses, possibly
affecting the academic viability of a program as well as the related required
resources. Providing a method that estimates this population could be of
substantial help to university management and academic personnel. We describe
how to use course precedence constraints to calculate alternative tuition paths
and then use Markov models to estimate future populations. In doing so, we
identify key issues of a large scale potential deployment.</description><identifier>DOI: 10.48550/arxiv.cs/0701174</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computers and Society ; Computer Science - Data Structures and Algorithms ; Computer Science - Symbolic Computation</subject><creationdate>2007-01</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/cs/0701174$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.cs/0701174$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Hadzilacos, T</creatorcontrib><creatorcontrib>Kalles, D</creatorcontrib><creatorcontrib>Koumanakos, D</creatorcontrib><creatorcontrib>Mitsionis, V</creatorcontrib><title>A Prototype for Educational Planning Using Course Constraints to Simulate Student Populations</title><description>Distance learning universities usually afford their students the flexibility
to advance their studies at their own pace. This can lead to a considerable
fluctuation of student populations within a program's courses, possibly
affecting the academic viability of a program as well as the related required
resources. Providing a method that estimates this population could be of
substantial help to university management and academic personnel. We describe
how to use course precedence constraints to calculate alternative tuition paths
and then use Markov models to estimate future populations. In doing so, we
identify key issues of a large scale potential deployment.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computers and Society</subject><subject>Computer Science - Data Structures and Algorithms</subject><subject>Computer Science - Symbolic Computation</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tqAjEARbNxUWw_oCvzA6OJeU2WMmhbEDqgLmXI5FECYzIkmVL_vtq6uQfu4sAB4BWjJa0ZQyuVfvz3UucVEghjQZ_AeQPbFEss19FCFxPcmkmr4mNQA2wHFYIPX_CU79vEKWV7Q8glKR9KhiXCg79MgyoWHspkbCiwjeP9uCnyM5g5NWT78uAcHHfbY_Ne7T_fPprNvlJc0IqshTSM9NQQqimRSBpdG82Nk5IQboXDteWmrwnqOdZryTiS3DLbY04ZdmQOFv_av75uTP6i0rXTuXt0kl97cU9i</recordid><startdate>20070126</startdate><enddate>20070126</enddate><creator>Hadzilacos, T</creator><creator>Kalles, D</creator><creator>Koumanakos, D</creator><creator>Mitsionis, V</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20070126</creationdate><title>A Prototype for Educational Planning Using Course Constraints to Simulate Student Populations</title><author>Hadzilacos, T ; Kalles, D ; Koumanakos, D ; Mitsionis, V</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-3279d53b4d34c43909dc8dc6df99336e7f18e6db830b61c2956096e5eb16451f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computers and Society</topic><topic>Computer Science - Data Structures and Algorithms</topic><topic>Computer Science - Symbolic Computation</topic><toplevel>online_resources</toplevel><creatorcontrib>Hadzilacos, T</creatorcontrib><creatorcontrib>Kalles, D</creatorcontrib><creatorcontrib>Koumanakos, D</creatorcontrib><creatorcontrib>Mitsionis, V</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hadzilacos, T</au><au>Kalles, D</au><au>Koumanakos, D</au><au>Mitsionis, V</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Prototype for Educational Planning Using Course Constraints to Simulate Student Populations</atitle><date>2007-01-26</date><risdate>2007</risdate><abstract>Distance learning universities usually afford their students the flexibility
to advance their studies at their own pace. This can lead to a considerable
fluctuation of student populations within a program's courses, possibly
affecting the academic viability of a program as well as the related required
resources. Providing a method that estimates this population could be of
substantial help to university management and academic personnel. We describe
how to use course precedence constraints to calculate alternative tuition paths
and then use Markov models to estimate future populations. In doing so, we
identify key issues of a large scale potential deployment.</abstract><doi>10.48550/arxiv.cs/0701174</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computers and Society Computer Science - Data Structures and Algorithms Computer Science - Symbolic Computation |
| title | A Prototype for Educational Planning Using Course Constraints to Simulate Student Populations |
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