Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the Spanish Ministry of Science and Innovation, project PID2020-115832GBI00, and the University of Cordoba, project UCO-FEDER 18 REF.1263116 MOD.A. Both projects were also supported by the European Fund of Regional Development.To provide a good study plan is key to avoid students’ failure. Academic advising based on student’s preferences, complexity of the semester, or even background knowledge is usually considered to reduce the dropout rate. This article aims to provide a good course index to recommend courses to students based on the sequence of courses already taken by each student. Hence, unlike existing long-term course planning methods, it is based on graduate students to model the course and not on external factors that might introduce some bias in the process. The proposal includes a novel sequential pattern mining algorithm, called (ES)2 P (Evolutionary Search of Emerging Sequential Patterns), that properly identifies paths followed by good students and not followed by not so good students, as a long-term course planning approach. A major feature of the proposed (ES)2 P algorithm is its ability to extract the best k solutions, that is, those with a best recommendation index score instead of returning the whole set of solutions above a predefined threshold. A real study case is performed including more than 13,000 students belonging to 13 faculties to demonstrate the usefulness of the proposal not only to recommend study plans but also to give advices at different stages of the students’ learning process.CRUE-CSICSpringer NatureSpanish Government PID2020-115832GBI00University of Cordoba UCO-FEDER 18 REF.1263116 MOD.

Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns

To provide a good study plan is key to avoid students’ failure. Academic advising based on student’s preferences, complexity of the semester, or even background knowledge is usually considered to reduce the dropout rate. This article aims to provide a good course index to recommend courses to students based on the sequence of courses already taken by each student. Hence, unlike existing long-term course planning methods, it is based on graduate students to model the course and not on external factors that might introduce some bias in the process. The proposal includes a novel sequential pattern mining algorithm, called (ES)2P (Evolutionary Search of Emerging Sequential Patterns), that properly identifies paths followed by good students and not followed by not so good students, as a long-term course planning approach. A major feature of the proposed (ES)2P algorithm is its ability to extract the best k solutions, that is, those with a best recommendation index score instead of returning the whole set of solutions above a predefined threshold. A real study case is performed including more than 13,000 students belonging to 13 faculties to demonstrate the usefulness of the proposal not only to recommend study plans but also to give advices at different stages of the students’ learning process

Matematički modeli za višekriterijumske procene u sistemima učenja na daljinu

Učenje na daljinu (eng. Distance Learning System - DLS) je sveprisutniji savremeni koncept obrazovanja. To je koncept zasnovan na tutorskom sistemu i njegovim modulima (moduli asistenta, tutora, moduli za praćenje i prepoznavanje emocija, motivacije, itd.) koji deluju u korelaciji sa povratnim informacijama iz sistema. Ova modularnost značajna je pre svega u korektivnoj ulozi, adaptivnom kapacitetu, automatizaciji i samoregulaciji DL sistema. Novi koncept DL sistema omogućio je lakše uvođenje, široku zastupljenost i veću funkcionalnost savremenih sistema za upravljanje učenjem (eng. Learning Management System - LMS). LMS sistemi koriste se za kreiranje, organizaciju, realizaciju, administraciju, verifikaciju kurseva u skladu sa obrazovnim okruženjem. Definisanje okruženja i priprema za nastavu počinje prepoznavanjem karakteristika studenata, nastavne grupe, podelom u podgrupe po orjentaciji, predznanju, predispozicijama, odnosno prepoznavanju idividualnih karakteristika, sklonosti i mogućnosti studenata. Pristupanjem kursu, tokom kursa, a posebno pri rešavanju problemskih zadataka, dolazi do nejasnoća i nerazumevanja, razvijaju se različite emocije, padovi koncentracije, motivacije, i sl. Tada DL sistem i pripadajući moduli moraju signalzirati nepravilnosti i slabosti koncepta učenja u skladu sa individualnim potrebama, a uz pomoć modula korektivnih funkcija prevazilaziti ih. Iz tog razloga vrlo je važno u startu indetifikovati validne činioce obrazovnog okruženja i karakteristike studenta. U skladu stim neophodno je grupi/pojedincu dodeliti adektavtan skupa modula DL sistemu za uspešno praćenje kursa i kvalitetne procene uspešnosti okončanja kursa. Različitost činioca koji definišu okruženje neophodno je definisati težinskim faktorom, obzorom na posledične efekte. Dakle, neophodno je izraditi matematičke modele za ocenjivanje rada studenata koji studiraju na daljinu, na osnovu ulaznih parametara sa jedne strane (opšti uspeh, uspeh iz srodnih predmeta prethodnog nivoa školovanja, motivisanost studenta za studije, i sl.), te na osnovu reakcija tokom kursa na zadate probleme i zadatke, dobijenih u cilju uspešnog savlađivanja gradiva, sa druge strane. Takođe, neophodno je izraditi matematičke modele višekriterijumskog (VK) ocenjivanja odgovarajućih platformi za DL studije sa ciljem da se odredi za date situacije odgovarajući način održavanja nastave za pojedine studente ili grupe. Prosta primena poznatih metoda VKA ponekad nije moguća, zbog same strukture metoda. Dakle, u cilju postizanja odgovarajućih rezultata, razvija se prilagođeni VK metoda za ocenjivanje za kokretni slučaj, tj. za kokretno obrazovno okruženje