Бидний тухай
Багш ажилтан
Элсэлтийн ерөнхий шалгалтын дүнг урьдчилан таамаглах нь шалгалтанд бэлтгэгчид болон шийдвэр гаргах түвшний байгуулагад үнэтэй мэдээлэл болдог. Судалганы ажлын зорилго нь ЕБС-ийн сурагчийн сурлагын дүн болон бусад хувийн өгөгдөлд тулгуурлан шалгалтын дүнг урьдчилан таамаглах боломжийг судлах юм. Ерөнхий боловсролын сургуулийн сүүлийн 3 жилийн сурлагын дүн, амьдрах орчны судалгаа, элсэлтийн ерөнхий шалгалтын дүнгээс тохирох онцлогийг тодорхойлж, хэд хэдэн регресс болон машин сургалтын техникийг ашигласан. Туршилтаар бидний боловсруулсан хамгийн сайн загвар нь 78.5 хувийн зөв таамагласан үр дүн үзүүлсэн.
Судалгааны ажлаар ажил олгогч болон ажил горилогчийн мэдээллийг ажлын зар болон анкетаас задлаж тухайн ажил олгогчид тохирох ажил горилогчийг олж санал болгох, нөгөө талаас ажил горилогчид хамгийн тохирох ажлын байрыг санал болгож чадах загварыг машин сургалт болон хайлтын алгоритмуудыг ашиглан бий болгох зорилго тавин ажиллалаа.
The research aims to create a model using machine learning and search algorithms that can parse information from job postings, and applicant resumes to recommend suitable candidates to employers and the most suitable jobs to applicants.
Goal of research work is to determine the most important features on predicting the grade of the General Entrance Exam (GEE). The features are high school student’s grade, personal information, and state exam results. We collected 96,827 high school students data and compared the F1 measures with different classification techniques such as decision tree, logistic regression, artificial neural network, and support vector machine. Among these techniques, the SVM provided the best F1 measure that is 0.70.
As education becomes increasingly digital, all educational institutions are collecting a lot of data, including quarterly exams, homework scores, and student personal information. But all this information used to make the final result of the student whether to pass or fail the particular examination. If student data is analyzed timely then definitely it will help the student to improve their performance in academics. Now a day's educational system is not limited to any formal teaching within a classroom but it goes beyond that like online MOOC course, Web-based education system (WBS), an intelligent tutorial system (ITS), Project-based learning (PBL), webinar, Seminar, Workshops and many more. All these teaching methods make the education system more attractive and provide lots of knowledge and learning through a different source. If all these methods do not properly check and evaluate then it adversely affect the education system. So for making any education system to have a success, then a proper evaluation check should be maintained. The ML techniques are used to analyze the historical data of any organization by using an inbuilt algorithm, which further finds the hidden information from that data which is not possible to find manually. By periodically evaluating the student progress, timely help and support are provided to those students who are on the risk of failure or dropout, so that they can improve their performance in the future.
