Mokymosi analitikos naudojimas matematikos ir gamtamokslinio ugdymo pamokose: pagrindinio ugdymo mokytojų patirtys
LTMokymosi analitika yra įvardinama kaip viena esminių mokymosi kokybės kiekvienam mokiniui užtikrinimo prielaidų, siejama su platesnėmis individualizuoto, diferencijuoto ir personalizuoto mokymosi organizavimo galimybėmis. Vienas iš Lietuvos švietimo prioritetų – Gamtos mokslų ir matematikos mokymo(si) individualizavimas ir personalizavimas – sietinas su Lietuvos švietimo prioritetu puoselėti mokyklose inovacijų kultūrą. Nors pripažįstama integruoto mokymo(si) reikšmė, siekiant mokinio gamtamokslinės ir matematikos kompetencijos tvaraus tobulinimo, kyla problemos sprendžiant integruoto gamtos mokslų ir matematikos organizavimo klausimus pamokose, ieškant tinkamiausių didaktikos sprendimų mokinio bei klasės lygmenyse. Mokymosi analitikos nauda šiuolaikinėje edukacijoje nėra abejojama, tačiau ugdymo praktikoje požiūris į ją nevienareikšmiškas: mokymosi analitikos įrankių paieška, jos naudojimo sistema, naudos besimokančiajam apibrėžtys. Pripažįstama, kad moksliniame mokymosi analitikos naudojimo gamtamoksliniame ugdyme diskurse esama mažai tyrimų, pedagoginės praktikos pavyzdžių, kuriuose analizuojamos skaitmeninių platformų su dirbtinio intelekto ir mokymosi analitikos įrankiais galimybės, mokytojų patirčių analizė. Atliktame kokybiniame tyrime (focus grupių diskusijos) su matematikos ir gamtos mokslų mokytojais, kurie turi sukaupę darbo su skaitmeninėmis platformomis ir dirbtinio intelekto grįstos mokymosi analitikos taikymo patirties, analizuotos mokymosi analitikos naudojimo galimybės pamokoje.Focus grupių dalyviai mokytojai, kurie 2021 m. rugsėjo – gruodžio mėn. dalyvavo projekte, skirtame mokymosi analitikos įrankių išbandymui gamtamokslinio ugdymo ir matematikos pamokose. Tyrimo rezultatai atskleidė, kad mokytojams nekelia abejonių skaitmeninių platformų, integruojančių dirbtiniu intelektu grįstą mokymosi analitiką, nauda, identifikuojant, mokinio (klasės) mokymosi spragas, mokymosi ypatumus, priimant duomenimis pagrįstus sprendimus dėl mokymosi diferencijavimo ir individualizavimo. Tyrimo dalyviai įžvelgė mokymosi analitikos duomenų svarbą, planuojant ir organizuojant integruotas matematikos ir gamtamokslinio ugdymo pamokos. [Iš leidinio]
ENLearning analytics is identified as one of the essential preconditions for ensuring the quality of learning for each student and is associated with the wider possibilities of organizing individualized learning. One of the priorities of Lithuanian education is the individualization and personalization of science and mathematics teaching, which is related to one of the priorities of Lithuanian education, that is recognizing the need to develop students' mathematics, science, and technology competencies as well as to foster a culture of innovation in schools. The importance of integrated teaching (learning) for the sustainable development of a student's science and mathematics competence is recognized. However, problems arise in addressing the issues of integrated science and mathematics organization in the classroom, in finding the most appropriate didactic solutions at the level of a student and a classroom. The benefits of learning analytics in modern education are not in doubt, but in educational practice the approach to it is ambiguous: the search for learning analytics tools, the system of its use, the definitions of benefits for the learner. It is acknowledged that in the discourse of the use of learning analytics in science education, there is little research, examples of pedagogical practice that contain analysis of the possibilities of digital platforms with artificial intelligence and learning analytics tools, and the analysis of teachers' experiences. In the conducted qualitative study (focus group discussion) with mathematics and science teachers, who have accumulated experience in working with digital platforms and applying artificial intelligence-based learning analytics, the possibilities of using learning analytics in the lesson have been disclosed. Focus groups participants are teachers who in 2021.September - December participated in a project with the aim to test learning analytics tools in science education and math lessons. The results of the study revealed that teachers do not question the benefits of integrating digital platforms with artificial intelligencebased learning analytics in identifying student (classroom) learning gaps, learning characteristics, and making evidence-based decisions about learning differentiation and individualization. The results of the focus group discussion with science education and mathematics teachers regarding the use of digital teaching and learning platforms integrating learning analytics in lessons revealed that the priority of learning analytics in lessons is to identify and capture gaps in students' learning achievements and knowledge in a timely manner. The analysis of a student (students) learning data that is provided by digital platforms, which integrate artificial intelligence and learning analytics, allows teachers to make the most appropriate decisions about the organization of teaching: to differentiate and individualize teaching, to consistently develop pupils' general competencies. The results of the discussion highlighted the benefits of learning analytics tools for the learner (students): learning analytics tools allow students to see personal progress; receive the tasks assigned to them individually; implement collaborative learning; engage (intellectually and emotionally) in learning activities; learn not only during lessons. An important criterion for the integration of mathematics and science lessons is the use of the learning analytics tools, the joint work of teachers in analyzing students' learning strengths and weaknesses, finding the best learning opportunities, and making similar or different lesson organization decisions.Participants of the study emphasized the importance of learning analytics data in planning and organizing integrated mathematics and science lessons, i.e. synergistic opportunities for learning analytics in the organization of integrated mathematics and science education. The results of the research do not allow making generalized conclusions that would be suitable for the whole Lithuania, however the results of the research revealed that the development of models for the application of learning analytics and the analysis of their effectiveness are important directions for further research. [From the publication]
