Autonomous Cognitive Leveling Game Pada Serious Game Menggunakan Particle Swarm Optimization

Abstract. Serious games containing the pedagogical aspects and as part of the device/media e-learning support the learning process. Besides, the learning method uses the game are better than the conventional learning, because learning materials that involve animation in the game will enable long-term memory of students. Particle swarm optimization (PSO) method offers a search procedure based on a population consisting of individuals called particles that change their position with respect to time. PSO, by way of initializing the position and velocity of a particle, calculates the fitness function of the solution and updates the position and velocity of a particle to a stop condition are found. The design of PSO on the problem of autonomous cognitive levels of the game on a serious game with a permutation is proposed by using the fitness function the distance between xi+1 (cognitive level game) with xi (cognitive pre-test). The expected outcome of this research is the sequence of levels completed in accordance with the needs of the learner.Keywords: Serious game, cognitive, pso Abstrak. Serious game sangat mendukung proses pembelajaran melalui permainan yang mengandung aspek pedagogis dan merupakan bagian dari alat/media e-learning. Selain itu metode pembelajaran menggunakan permainan lebih baik dibandingkan dengan pembelajaran konvensional, karena animasi materi pembelajaran dalam permainan akan mengaktifkan ingatan jangka panjang siswa.Metode particle swarm optimization (PSO) menawarkan suatu prose­dur pen­­ca­rian berdasar pada populasi yang terdiri atas individu-individu yang di­se­but par­­tikel, mengubah posisi mereka terhadap waktu. PSO dengan cara melakukan inisialisasi posisi dan kecepatan particle, menghitung fungsi fitness dari solusi dan mengupdate posisi dan kecepatan particle sampai kondisi berhenti ditemukan.Perancanagan PSO pada permasalahan autonomus cognitive level game pada serious game diusulkan menggunakan permutasi dengan fungsi fitness jarak antara xi+1(cognitive level game) dengan xi (cognitive pre-test).Hasil yang diharapkan dari penelitian ini adalah adanya urutan level game yang sesuai dengan kebutuhan pembelajar.Kata Kunci: Serious game, cognitive, pso

Kesan penyesuaian pembelajaran berdasarkan gaya pembelajaran atas talian terhadap pembentukan pengetahuan pelajar

Learning style is personal parameter which could increase students’ achievement. Recent studies have shown that Adaptive Learning Based on Learning Style (PPGP) increased students’ achievement. However, information on achievement does not explain the process of knowledge construction during learning process. Thus, this study aims to investigate the effect of PPGP on the students’ achievement and knowledge constructions. A sampel for this research consists of Diploma students in Electrical Engineering (Computer) who take Multimedia Interactive Application subject at a polytechnic. This research has two samples: set I (130 students) was involved to survey pre-learning style using Felder and Solomon’s questionnaire and set II (35 students) was involved in learning through Learning Management Sytem (LMS). This research has used pre-experimental design with one-group pretest-posttest. Before the treatment, students were given a pre test and LMS without PPGP treatment for 8 weeks to determine online learning style by using automatic approach. Then, for another 6 weeks, the same sample was given LMS with PPGP with active students were given Group-Problem Solving (PPGPPM) and reflective students were given Introspective-Guided Inquiry (PPGP-IT). At the end of the treatment, students were given post achievement tests. Paired-Samples T test was used to investigate the effect of LMS with PPGP on students’ achievement and whereas, its effect on students' knowledge construction was analysed by using content analysis. Next, sequential analysis was used to obtain model of knowledge construction process based on navigational behaviour sequence during learning process. The result shows LMS with PPGP has increased students’ achievement (p=0.000, a=0.05) with the effect size (Cohen d = 2.869) shows LMS with PPGP has given a large effect size on sudents’ achievement in the test. Although the test was repeated several times, the power value is 1.00 showing the same result will be obtained. The result also indicates, the highest level of knowledge construction is 34.49%, which is at integration level. This research also produced a process model of knowledge construction based on potential navigation behaviour that can assist students to achieve high level of knowledge construction based on learning style. In conclusion, LMS with PPGP increases achievement and helps students to achieve higher level of knowledge construction

Metamodel for personalized adaptation of pedagogical strategies using metacognition in Intelligent Tutoring Systems

The modeling process of metacognitive functions in Intelligent Tutoring Systems (ITS) is a difficult and time-consuming task. In particular when the integration of several metacognitive components, such as self-regulation and metamemory is needed. Metacognition has been used in Artificial Intelligence (AI) to improve the performance of complex systems such as ITS. However the design ITS with metacognitive capabilities is a complex task due to the number and complexity of processes involved. The modeling process of ITS is in itself a difficult task and often requires experienced designers and programmers, even when using authoring tools. In particular the design of the pedagogical strategies for an ITS is complex and requires the interaction of a number of variables that define it as a dynamic process. This doctoral thesis presents a metamodel for the personalized adaptation of pedagogical strategies integrating metamemory and self-regulation in ITS. The metamodel called MPPSM (Metamodel of Personalized adaptation of Pedagogical Strategies using Metacognition in intelligent tutoring systems) was synthetized from the analysis of 40 metacognitive models and 45 ITS models that exist in the literature. MPPSMhas a conceptual architecture with four levels of modeling according to the standard Meta- Object Facility (MOF) of Model-Driven Architecture (MDA) methodology. MPPSM enables designers to have modeling tools in early stage of software development process to produce more robust ITS that are able to self-regulate their own reasoning and learning processes. In this sense, a concrete syntax composed of a graphic notation called M++ was defined in order to make the MPPSM metamodel more usable. M++ is a Domain-Specific Visual Language (DSVL) for modeling metacognition in ITS. M++ has approximately 20 tools for modeling metacognitive systems with introspective monitoring and meta-level control. MPPSM allows the generation of metacognitive models using M++ in a visual editor named MetaThink. In MPPSM-based models metacognitive components required for monitoring and executive control of the reasoning processes take place in each module of an ITS can be specified. MPPSM-based models represent the cycle of reasoning of an ITS about: (i) failures generated in its own reasoning tasks (e.g. self-regulation); and (ii) anomalies in events that occur in its Long-Term Memory (LTM) (e.g. metamemory). A prototype of ITS called FUNPRO was developed for the validation of the performance of metacognitive mechanism of MPPSM in the process of the personalization of pedagogical strategies regarding to the preferences and profiles of real students. FUNPRO uses self-regulation to monitor and control the processes of reasoning at object-level and metamemory for the adaptation to changes in the constraints of information retrieval tasks from LTM. The major contributions of this work are: (i) the MOF-based metamodel for the personalization of pedagogical strategies using computational metacognition in ITS; (ii) the M++ DSVL for modeling metacognition in ITS; and (iii) the ITS prototype called FUNPRO (FUNdamentos de PROgramación) that aims to provide personalized instruction in the subject of Introduction to Programming. The results given in the experimental tests demonstrate: (i) metacognitive models generated are consistent with the MPPSM metamodel; (ii) positive perceptions of users with respect to the proposed DSVL and it provide preliminary information concerning the quality of the concrete syntax of M++; (iii) in FUNPRO, multi-level pedagogical model enhanced with metacognition allows the dynamic adaptation of the pedagogical strategy according to the profile of each student.Doctorad