34,225 research outputs found
School-leavers' Transition to Tertiary Study: a Literature Review.
The theoretical and empirical literature relating to factors and problems in the transition of students from secondary to tertiary level education is reviewed here. Studies on persistence and attrition, and on the analysis and prediction of academic performance of students, generally and in particular discipline areas, are included.Transition to university; student performance.
Attrition in STEM Fields at a Liberal Arts College: The Importance of Grades and Pre-Collegiate Preferences
There is widespread concern, both in the private and public sectors, about perceived declines in U.S. college graduates in STEM fields. In our sample, the proportion of science majors has remained steady over the sample period; however, the number entering our college intending to major in STEM fields has fallen. In this paper we use administrative data from the graduating classes of 2001-2009, roughly 5000 graduates, from a northeastern liberal arts college to model the progression of students through STEM majors. A series of selection models predicts the choice of whether to take a second course in the department, conditional upon having taken a first course. This choice is modeled as a function of pre-college characteristics and preferences, characteristics of the student, the course, the professor, the peers in the course, and the grade received in the course. Using the selected sample that progresses to a second course, the choice to progress to a third is modeled conditional on having taken the second. The covariates in these models are similar to those in the first stage. Models are estimated for the Biology, Chemistry, Computer Science, Geology, Mathematics, Physics, and Psychology majors. Results suggest that gender effects are important, both in terms of the influence of the absolute and relative grades received, and in some cases in terms of the peers in the course and the gender of the instructor. The intended major (as reported on the admissions application) is a strong indicator of the likelihood of taking initial courses in a discipline and progression to a second course. AP credits are also strongly correlated to taking a first course, but diminish in the more selected samples. Grades and pre-collegiate intended major, have the most consistent and important influence on the decision to progress in a STEM major. When comparing across men and women, grades play a more important role in menâs decision-making while preferences play a bigger role in womenâs choices
Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences
This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering
Sixteen years of Collaborative Learning through Active Sense-making in Physics (CLASP) at UC Davis
This paper describes our large reformed introductory physics course at UC
Davis, which bioscience students have been taking since 1996. The central
feature of this course is a focus on sense-making by the students during the
five hours per week discussion/labs in which the students take part in
activities emphasizing peer-peer discussions, argumentation, and presentations
of ideas. The course differs in many fundamental ways from traditionally taught
introductory physics courses. After discussing the unique features of CLASP and
its implementation at UC Davis, various student outcome measures are presented
showing increased performance by students who took the CLASP course compared to
students who took a traditionally taught introductory physics course. Measures
we use include upper-division GPAs, MCAT scores, FCI gains, and MPEX-II scores.Comment: Also submitted to American Journal of Physic
Predictive models as early warning systems for student academic performance in introductory programming
Computer programming is fundamental to Computer Science and IT curricula. At the novice level it covers programming concepts that are essential for subsequent advanced programming courses. However, introductory programming courses are among the most challenging courses for novices and high failure and attrition rates continue even as computer science education has seen improvements in pedagogy. Consequently, the quest to identify factors that affect student learning and academic performance in introductory computer programming courses has been a long-standing activity. Specifically, weak novice learners of programming need to be identified and assisted early in the semester in order to alleviate any potential risk of failing or withdrawing from their course. Hence, it is essential to identify at-risk programming students early, in order to plan (early) interventions.
The goal of this thesis was to develop a validated, predictive model(s) with suitable predictors of student academic performance in introductory programming courses. The proposed model utilises the NaĂŻve Bayes classification machine learning algorithm to analyse student performance data, based on the principle of parsimony. Furthermore, an additional objective was to propose this validated predictive model as an early warning system (EWS), to predict at-risk students early in the semester and, in turn, to potentially inform instructors (and students) for early interventions.
We obtained data from two introductory programming courses in our study to develop and test the predictive models. The models were built with student presage and in progress-data for which instructors may easily collect or access despite the nature of pedagogy of educational settings. In addition, our work analysed the predictability of selected data sources and looked for the combination of predictors, which yields the highest prediction accuracy to predict student academic performance. The prediction accuracies of the models were computed by using confusion matrix data including overall model prediction accuracy, prediction accuracy sensitivity and specificity, balanced accuracy and the area under the ROC curve (AUC) score for generalisation.
On average, the models developed with formative assessment tasks, which were partially assisted by the instructor in the classroom, returned higher at-risk prediction accuracies than the models developed with take-home assessment task only as predictors. The unknown data test results of this study showed that it is possible to predict 83% of students that need support as early as Week 3 in a 12-week introductory programming course. The ensemble method-based results suggest that it is possible to improve overall at-risk prediction performance with low false positives and to incorporate this in early warning systems to identify students that need support, in order to provide early intervention before they reach critical stages (at-risk of failing).
The proposed model(s) of this study were developed on the basis of the principle of parsimony as well as previous research findings, which accounted for variations in academic settings, such as academic environment, and student demography. The predictive model could potentially provide early warning indicators to facilitate early warning intervention strategies for at-risk students in programming that allow for early interventions. The main contribution of this thesis is a model that may be applied to other programming and non-programming courses, which have both continuous formative and a final exam summative assessment, to predict final student performance early in the semester.Ohjelmointi on informaatioteknologian ja tietojenkÀsittelytieteen opinto-ohjelmien olennainen osa. Aloittelijatasolla opetus kattaa jatkokurssien kannalta keskeisiÀ ohjelmoinnin kÀsitteitÀ. TÀstÀ huolimatta ohjelmoinnin peruskurssit ovat erÀitÀ haasteellisimmista kursseista aloittelijoille. Korkea keskeyttÀmisprosentti ja opiskelijoiden asteittainen pois jÀttÀytyminen ovat vielÀkin tunnusomaisia piirteitÀ nÀille kursseille, vaikka ohjelmoinnin opetuksen pedagogiikka onkin kehittynyt. NÀin ollen vaikuttavia syitÀ opiskelijoiden heikkoon suoriutumiseen on etsitty jo pitkÀÀn. Erityisesti heikot, aloittelevat ohjelmoijat tulisi tunnistaa mahdollisimman pian, jotta heille voitaisiin tarjota tukea ja pienentÀÀ opiskelijan riskiÀ epÀonnistua kurssin lÀpÀimisessÀ ja riskiÀ jÀttÀÀ kurssi kesken. Heikkojen opiskelijoiden tunnistaminen on tÀrkeÀÀ, jotta voidaan suunnitella aikainen vÀliintulo.
TÀmÀn vÀitöskirjatyön tarkoituksena oli kehittÀÀ todennettu, ennustava malli tai malleja sopivilla ennnustusfunktioilla koskien opiskelijan akateemista suoriutumista ohjelmoinnin peruskursseilla. Kehitetty malli kÀyttÀÀ koneoppivaa naiivia bayesilaista luokittelualgoritmia analysoimaan opiskelijoiden suoriutumisesta kertynyttÀ aineistoa. LÀhestymistapa perustuu yksinkertaisimpien mahdollisten selittÀvien mallien periaatteeseen. LisÀksi, tavoitteena oli ehdottaa tÀtÀ validoitua ennustavaa mallia varhaiseksi varoitusjÀrjestelmÀksi, jolla ennustetaan putoamisvaarassa olevat opiskelijat opintojakson alkuvaiheessa sekÀ informoidaan ohjaajia (ja opiskelijaa) aikaisen vÀliintulon tarpeellisuudesta.
KerÀsimme aineistoa kahdelta ohjelmoinnin peruskurssilta, jonka pohjalta ennustavaa mallia kehitettiin ja testattiin. Mallit on rakennettu opiskelijoiden ennakkotietojen ja kurssin kestÀessÀ kerÀttyjen suoriutumistietojen perusteella, joita ohjaajat voivat helposti kerÀtÀ tai joihin he voivat pÀÀstÀ kÀsiksi oppilaitoksesta tai muusta ympÀristöstÀ huolimatta. LisÀksi vÀitöskirjatyö analysoi valittujen datalÀhteiden ennustettavuutta ja sitÀ, mitkÀ mallien muuttujista ja niiden kombinaatioista tuottivat kannaltamme korkeimman ennustetarkkuuden opiskelijoiden akateemisessa suoriutumisessa. Mallien ennustusten tarkkuuksia laskettiin kÀyttÀmÀllÀ sekaannusmatriisia, josta saadaan laskettua ennusteen tarkkuus, ennusteen spesifisyys, sensitiivisyys, tasapainotettu tarkkuus sekÀ luokitteluvastekÀyriÀ (receiver operating characteristics (ROC)) ja nÀiden luokitteluvastepinta-ala (area under curve (AUC))
Mallit, jotka kehitettiin formatiivisilla tehtÀvillÀ, ja joissa ohjaaja saattoi osittain auttaa luokkahuonetilanteessa, antoivat keskimÀÀrin tarkemman ennustuksen putoamisvaarassa olevista opiskelijoista kuin mallit, joissa kÀytettiin kotiin vietÀviÀ tehtÀviÀ ainoina ennusteina. Tuntemattomalla testiaineistolla tehdyt mallinnukset osoittavat, ettÀ voimme tunnistaa jo 3. viikon kohdalla 83% niistÀ opiskelijoista, jotka tarvitsevat lisÀtukea 12 viikkoa kestÀvÀllÀ ohjelmoinnin kurssilla. Tulosten perusteella vaikuttaisi, ettÀ yhdistÀmÀllÀ metodeja voidaan saavuttaa parempi yleinen ennustettavuus putoamisvaarassa olevien opiskelijoiden suhteen pienemmÀllÀ mÀÀrÀllÀ vÀÀrin luokiteltuja epÀtositapauksia. Tulokset viittaavat myös siihen, ettÀ on mahdollista sisÀllyttÀÀ yhdistelmÀmalli varoitusjÀrjestelmiin, jotta voidaan tunnistaa avuntarpeessa olevia opiskelijoita ja tarjota tÀten varhaisessa vaiheessa tukea ennen kuin on liian myöhÀistÀ.
TÀssÀ tutkimuksessa esitellyt mallit on kehitetty nojautuen yksinkertaisimman selittÀvÀn mallin periaatteeseen ja myös aiempiin tutkimustuloksiin, joissa huomioidaan erilaiset akateemiset ympÀristöt ja opiskelijoiden tausta. Ennustava malli voi tarjota indikaattoreita, jotka voivat mahdollisesti toimia pohjana vÀliintulostrategioihin kurssilta putoamisvaarassa olevien opiskelijoiden tukemiseksi. TÀmÀn tutkimuksen keskeisin anti on malli, jolla opiskelijoiden suoriutumista voidaan arvioida muilla ohjelmointia ja muita aihepiirejÀ kÀsittelevillÀ kursseilla, jotka sisÀltÀvÀt sekÀ jatkuvaa arviointia ettÀ loppukokeen. Malli ennustaisi nÀillÀ kursseilla lopullisen opiskelijan suoritustason opetusjakson alkuvaiheessa
The Longitudinal Effects of STEM Identity and Gender on Flourishing and Achievement in College Physics
Background. Drawing on social identity theory and positive psychology, this study investigated womenâs responses to the social environment of physics classrooms. It also investigated STEM identity and gender disparities on academic achievement and flourishing in an undergraduate introductory physics course for STEM majors. 160 undergraduate students enrolled in an introductory physics course were administered a baseline survey with self-report measures on course belonging, physics identification, flourishing, and demographics at the beginning of the course and a post-survey at the end of the academic term. Students also completed force concept inventories and physics course grades were obtained from the registrar.
Results. Women reported less course belonging and less physics identification than men. Physics identification and grades evidenced a longitudinal bidirectional relationship for all students (regardless of gender) such that when controlling for baseline physics knowledge: (a) students with higher physics identification were more likely to earn higher grades; and (b) students with higher grades evidenced more physics identification at the end of the term. Men scored higher on the force concept inventory than women, although no gender disparities emerged for course grades. For women, higher physics (versus lower) identification was associated with more positive changes in flourishing over the course of the term. High-identifying men showed the opposite pattern: negative change in flourishing was more strongly associated with high identifiers than low identifiers.
Conclusions. Overall, this study underlines gender disparities in physics both in terms of belonging and physics knowledge. It suggests that strong STEM identity may be associated with academic performance and flourishing in undergraduate physics courses at the end of the term, particularly for women. A number of avenues for future research are discussed
Introductory programming: a systematic literature review
As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming.
This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research
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Scholarly insight Autumn 2017:a Data wrangler perspective
As the OU is going through several fundamental changes, it is important that strategic decisions made by Faculties and senior management are informed by evidence-based research and insights. One way how Data Wranglers provide insights of longitudinal development and performance of OU modules is the Key Metric Report 2017. A particular new element is that data can now also be unpacked and visualised on a Nation-level. As evidenced by the Nation-level reporting, there are substantial variations of success across the four Nations, and we hope that our interactive dashboards allow OU staff to unpack the underlying data.
The second way Data Wranglers provide insight to Faculties and Units is through the Scholarly insight report series. Building on the previous two reports whereby we reported on substantial variation and inconsistencies in learning designs and assessment practices within qualifications across the OU, in this Scholarly insight Autumn 2017 report we address four big pedagogical questions that were framed and co-constructed together with the Faculties and LTI units. Many Faculties and colleagues have reacted positively on our Scholarly insight Spring 2017 report, whereby for the first time we were able to show empirically that students experienced substantial variations in success within 12 large OU qualifications. As evidenced in our previous report, 55% of variation in studentsâ success over time was explained by OU institutional factors (i.e., how students were assessed within their respective module; how students were able to effectively transition from one learning design of one module to the next one), rather than studentsâ characteristics, engagement and behaviour.
We have received several queries and questions from Faculties and Units about how to better understand these studentsâ journeys, and how qualifications and module designs could be better aligned within their respective qualification(s). As these are complex conceptual and Big Pedagogy questions, in Chapter 1 we continued these complex analyses by looking at the transitional processes of the first two modules that OU students take, and how well aligned these modules and qualification paths are. In Chapter 2, we explored the more fine-grained, qualitative, and lived experiences of 19 students across a range of qualifications to understand how OU grading practices and (in)consistencies of assessment and feedback influenced their affect, behaviour, and cognition. In addition to building on previous topics, we introduced two new Scholarly insights in Chapter 3 and Chapter 4. As the OU is increasingly using learning analytics to support our staff and students, in Chapter 3 we analysed the impact of giving Predictive Learning Analytics to over 500 Associate Lecturers across 31 modules on student retention. Finally, in Chapter 4 we explored the impact of first presentations of new modules on pass rates and satisfaction, whereby we were able to bust another myth that may have profound implications for Student First Transformation.
Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties , we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight
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