Individual Learning Models Research Articles

PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM ASSISTED INDIVIDUALIZATION TERHADAP HASIL BELAJAR SISWA KELAS X PADA MATA PELAJARAN MENERAPKAN DASAR-DASAR TEKNIK DIGITAL TEKNIK AUDIO VIDEO DI SMKN 1 BATIPUH

The problem in this study is the low average learning outcomes of students on subjects Applying Digital Engineering Basics is under the Minimum Criterion of completeness which has been established SMKN 1 Batipuh is 75. This study aims to determine the effect of application of Team Assisted Individualization model to learning outcomes on subjects Applying Digital Engineering Basics in Audio Video Techniques department at SMKN 1 Batipuh. The type of this research is experimental research with pre experimental design with intact group comparison design. The sample of research is class X TAV 2A as experiment class using Team Assisted Individualization model and class X TAV 2B as control class using direct learning model. Data is collected from post-test in the experimental class and control class, then analyzed for homogeneity, normality and hypothesis test. From the experimental class results obtained an average value of 80.71, while the control class gets an average value of 75.79. The result of hypothesis calculation at significant level α = 0,05 got tcount> ttable that is 2,442 > 2,056, because big tcount of ttable, null hypothesis (H0) rejected and alternative hypothesis (Ha) accepted.itCan be concluded mean at the real level, this research shows that, there is work on of student learning result which signifikan between usage of Team Assisted Individualization type with direct learning model on subjects Applying Digital Engineering Basics of student of Audio Video Technique at SMKN 1 Batipuh. So the Team Assisted Individualization learning model is better than the direct learning model. Keywords: Applying Digital Engineering Basics, Team Assisted Individualization model, direct learning model, pre experimental design, learning outcomes.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model.

Peningkatan Teknik Shooting Melalui Model Pembelajaran Team Assisted Individualization (TAI)

The intention of this research is to know the influence of Team Assisted Individualization (TAI) learning model to increase students’ shooting using instep part in football. The method of this research was experimental method. The population that was taken was the entire tenth grade students of SMK PASUNDAN 2 Cimahi consisted of 51 students. Using cluster sampling technique, it was obtained sample as many as 26 participants which were divided into two groups: experimental group (Team Assisted Individualization (TAI) learning model and control group (conventional). Based on the data analysis, students’ shooting in experiment group is better than control group (6.32 > ttable 1.80). Meanwhile, the statistical calculation showed the mean score of experiment group is bigger than control group (43.31>4.07). The conclusion is Team Assisted Individualization (TAI) learning method gives significant influence to increase students’ shooting using instep part in football.

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

Abstract Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis. In this paper, a novel method called ensemble deep auto-encoders (EDAEs) is proposed for intelligent fault diagnosis of rolling bearings. Firstly, different activation functions are employed as the hidden functions to design a series of auto-encoders (AEs) with different characteristics. Secondly, EDAEs are constructed with various auto-encoders for unsupervised feature learning from the measured vibration signals. Finally, a combination strategy is designed to ensure accurate and stable diagnosis results. The proposed method is applied to analyze the experimental bearing vibration signals. The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.

Learning Line and Angle at 7th Grade Students

The purpose of this research is to compare two learning model to teach line and angle, that is Numbered Head Together (NHT) model combined with Make A Match and Team Assisted Individualization (TAI) model. This research was quasi experimental research with factorial design 2×3. Population on this research were junior high school students on 7th grade, with 156 students as a sample. Results showed that students that taugh by Numbered Head Together (NHT) combined with Make A Match learning have better achievement than student that taugh by Team Assisted Individualization (TAI) learning model, student with high creative attitude have better achievement than student with medium creative attitude and low creative attitude, student with medium creative attitude have same achievement with student with low creative attitude. There is no interaction between learning model and creative attitude.

Timing under individual evolutionary learning in a continuous double auction

The moment of order submission plays an important role for the trading outcome in a Continuous Double Auction; submitting an offer at the beginning of the trading period may yield a lower profit, as the trade is likely to be settled at the own offered price, whereas late offers result in a lower probability of trading. This timing problem makes the order submission strategy more difficult. We extend the behavioral model of Individual Evolutionary Learning to incorporate the timing problem and study the limiting distribution of submission moments and the resulting offer function that maps submission moments to offers. We find that traders submit different offers at different submission moments the distribution of which uni-modal with a peak moving from late to early as the market size increases. This behavior exacerbates efficiency loss from learning. If traders evaluate profitability of their strategies over longer history, orders are submitted later with the same effect of market size.

Statistically Modeling Individual Students’ Learning Over Successive Collaborative Practice Opportunities

Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors model) to account for the effect of collaboration on individual learning, including having the help of a partner and getting to observe/help a partner. We find evidence that models that include these collaborative features have a better fit than the original models for performance data and that learning rates estimated using the extended models provide insights into how collaboration benefits individual students’ learning outcomes.

Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation

Probabilistic forecasting accounts for the uncertainty in prediction that arises from inaccurate input data due to measurement errors, as well as the inherent inaccuracy of a prediction model. Because of the variable nature of renewable power generation depending on weather conditions, probabilistic forecasting is well suited to it. For a grid-tied solar farm, it is increasingly important to forecast the solar power generation several hours ahead. In this study, we propose three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h ahead solar power forecasts. We have shown that while all of the individual machine learning models are more accurate than the traditional benchmark models, like autoregressive integrated moving average (ARIMA), the ensemble models offer even more accurate results than any individual machine learning model alone does. Furthermore, it is observed that running separate models on the data belonging to the same hour of the day vastly improves the accuracy of the results. Getting more accurate forecasts will help the stakeholders come up with better decisions in resource planning and control when large-scale solar farms are integrated into the power grid.

Lifelong learning through the SkillsFuture movement in Singapore: challenges and prospects

This article examines the promotion of lifelong learning (LLL) in Singapore through a new national initiative known as the SkillsFuture movement. It is argued that the attainment of LLL is confronted with three key challenges, the first being the sociocultural preference for academic rather than vocational education in Singapore. Secondly, there is an absence of a strong local culture that underscores the habits of mind needed for LLL. The final challenge is the dominant ideology of pragmatism that potentially conflicts with the goal of the SkillsFuture movement for individuals to enjoy learning and pursue their passion. The article further recommends a reconceptualisation of the notion of LLL in Singapore by supplementing the skills growth model with the individual development and social learning models. The Singapore example illustrates the difficulties and prospects of advocating LLL due to historical and socialcultural conditions and practices.

PENGEMBANGAN MODEL LESSON STUDY UNTUK SEKOLAH INKLUSI SMP NEGERI DI KOTA METRO

Several studies have shown that collaboration among teachers in Lesson Study (LS ) activities was able to create the quality of learning mathematics more professional. Along with this, it turns out collaboration between partners becomes essential for the success of teaching special needs students (ABK) i n inclusion class. Thus, in this study, research er try to apply LS activities in the implementation of mathematics in the classroom. The aim of t his study was to find out matters relating to: (1) Obtaining a complete description of the needs of the crew in mathematics, (2) Obtain an overview of the implementation lesson Study model in inclusion school, (3) Obtain package individual learning model (individual Educational Program [IEP]) for ABK in junior high school of mathematics inclusions, and (4) T he implementation of the LS models in mathematics learning in the inclusion classroom. This study use the development (research development) method which consisting of five stages. In the first stage is study of literature, planning and development. In the second stage conducted LS implementation, revision, and expansion. The findings of this research are expected to be used as a reference in the implementation of LS in the inclusi on classroom. In addition, provide input to the local government of Metro City in educational policy, especially in the organization of inclusion.

Pengaruh Model Pembelajaran Kooperatif Tipe Team Assisted Individualization dan Self-Efficacy terhadap Kemampuan Pemecahan Masalah Fisika Siswa di MTs N Kota Jambi

Mathematical problem-solving skills in learning physics is important. According to the preliminary study conducted at MTsN Jambi, it is known that the mathematical problem solving ability of students in learning physics still low. This study aims to improve the mathematical problem solving capability by applying the team assisted individualization and self-efficacy cooperative learning model. This study was experimental research by using questionnaire self-efficacy instrument and essay test. The population of this study was seventh grade students MTsN Jambi. Data analysis was performed by using two ways-ANOVA and Tukey test. The results shows that: (1) cooperative learning model of TAI influences to students’ physical problem solving capability, (2) SE influences the students’ physical problem solving capability, (3) there are differences in high SE students’ physical problem solving capability by using cooperative learning model type TAI from those who using conventional learning models, (4) there are differences in low SE students’ physical problem solving capability by using cooperative learning model TAI type than those using conventional learning model, (5) there is no interaction between the cooperative learning model of TAI and SE of students in MTsN Jambi.

EKSPERIMENTASI MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM ASSISTED INDIVIDUALIZATION DENGAN SCAFFOLDING BERBASIS MODUL PADA MATERI GEOMETRIDIMENSI TIGA DITINJAUDARI KEMANDIRIAN BELAJAR SISWA SMK KELAS XI DI KABUPATEN SRAGEN

The purposes of this research were to investigate: (1) which learning models of Team Assisted Individualization learning model with scaffolding based on module (TAI-S), Team Assisted Individualization learning model (TAI), or direct learning model (DL) results in a better learning achievement in the material of three-dimensional geometry; (2) which independence category of student learning, high, medium or low results in a better learning achievement on the material of three-dimensional geometry; (3) in each category of student learning independence, which learning models of the TAI-S, TAI, or DL model results in better learning achievement on the material of three-dimensional geometry. This research used the quasi experimental method with the factorial design of 3x3. Its population was all the students in Grade XI of Vocational High Schools in Sragen regency. The samples of the research were taken by using the stratified random sampling technique. The data of the research were gathered through documentation, questionnaire, and test. The documentation was employed to investigate the scores of semester test in Mathematics of the students in Semester 1, Academic Year 2012/2013, and was used for balance test among the classes exposed to the TAI-S, TAI, and DL models. The questionnaire was used to find out the independence category of student learning. The test was used to know the students learning achievement in Mathematics with material of three-dimensional geometry. The data of the research were analyzed by using the unbalanced two-way analysis of variance at the significance level of 5%. The results of the research are as follows: (1) the TAI-S learning model result in a better learning achievement than both the TAI and DL models. There are no any differences in the learning achievement of the students with the TAI learning model and DL model. (2) the students with the high independence category result in better learning achievement than students with medium and low independence category. The students with medium independence category result in better learning achievement than students in low independence category (3) in each category of student learning independence, based on the material of three-dimensional geometry, the TAI-S learning model, TAI learning model and DL model do not have correlation between one and another. Keywords : TAI-S learning model, TAI learning model, DL learning. Three Dimensional Geometry, Learning Independence.

A decade of CSCL

This issue of ijCSCL completes a decade of publication of CSCL research. When the field of Computer-Supported Collaborative Learning emerged about a decade prior to the launching of the journal, there was a pervasive sense of a paradigm revolution in learning research (Koschmann 1996). It was time to transcend cognitive science’s critique of behaviorism, extending the unit of cognition beyond the boundaries of the individual mind (Stahl 2015c). For instance, new directions in theory surfaced around the influential Institute for Learning Research (Brown et al., 1989; Lave & Wenger 1991; Orr 1990; Suchman 1987; Teasley & Roschelle 1993; Winograd & Flores 1986), as well as in distributed cognition (Hutchins 1996), activity theory (Engestrom 1987) and conversation analysis (Goodwin D Damsa 2014; Jones et al., 2006; Overdijk et al., 2014; Stahl 2012; 2013a). Cognitive science argued that human behavior could not be modeled without hypothesizing cognitive structures like mental representations and computations, which mediated responses to the environment, particularly linguistic responses (Chomsky 1959). However, the cognitive approach did not consider structures of interaction at the dialogical, small-group and community level, where language is primarily learned and practiced. Artificial intelligence—which exerted an important influence on CSCL—had already conceptualized cognition as taking place outside the human mind, in software computations and digital representations. The new post-cognitive theories began to postulate structures and practices at the group level, although they did not always clearly distinguish small groups from larger social institutions. Dialogical, interactional and practice-oriented approaches in CSCL have elaborated these conceptions within the context of collaborative learning (Cress & Kimmerle 2008; Ligorio et al., 2013; Oner 2013; Stahl et al., 2014). While the post-cognitive paradigm dominates CSCL theory and seems particularly appropriate to a field focused on group collaboration, it has perhaps been less influential in the areas of technology design and analysis methodology. CSCL research often relies upon technologies designed on a model of individual learning and knowledge transfer, rather than on an Intern. J. Comput.-Support. Collab. Learn (2015) 10:337–344 DOI 10.1007/s11412-015-9222-2

English

Nowadays, most of the virtual learning environments have been shown an individual learning model for those students who live in a specifically social context and this may generate some difficulties on its knowledge acquirement, as long as they are the result of collective social process. Some virtual environment learning models offer a dynamic Social Network grasp that promotes the communication of the participants. This tender involves the construction of a Didactic ICT unit comparing a LMS (Learning Management System) and a Social Network approach about geological ages, directed to ninth grade students at the Institucion Educativa Concejo de Sabaneta, a small municipality in Antioquia. This article centers its purpose on making a didactic unit measure for platforms as Moodle and Edmodo, against qualitative aspects as motivation, accessibility and the

Development of Multi-Level Holistic Model in Integrated Learning

When providing for a group learning situation where each student was at their own machine, the need arose to combine individual and group learning models to provide an educational framework for a supportive learning environment. The framework had to be implementable on a (reductionist) computer network. The independence of behaviour and implementation was exploited to allow a holistic educational framework to be used when designing the learning environment. In a model, memory, functions, and goals are reducible, whereas the relationship between the purpose functions and the state manipulating functions are irreducible. Implementation was neatly achievable using the software engineering technology of object oriented technology to provide encapsulation for the reducible aspects while preserving holistic entities that can exploit the benefits of polymorphism.

Some dynamics of signaling games.

Information transfer is a basic feature of life that includes signaling within and between organisms. Owing to its interactive nature, signaling can be investigated by using game theory. Game theoretic models of signaling have a long tradition in biology, economics, and philosophy. For a long time the analyses of these games has mostly relied on using static equilibrium concepts such as Pareto optimal Nash equilibria or evolutionarily stable strategies. More recently signaling games of various types have been investigated with the help of game dynamics, which includes dynamical models of evolution and individual learning. A dynamical analysis leads to more nuanced conclusions as to the outcomes of signaling interactions. Here we explore different kinds of signaling games that range from interactions without conflicts of interest between the players to interactions where their interests are seriously misaligned. We consider these games within the context of evolutionary dynamics (both infinite and finite population models) and learning dynamics (reinforcement learning). Some results are specific features of a particular dynamical model, whereas others turn out to be quite robust across different models. This suggests that there are certain qualitative aspects that are common to many real-world signaling interactions.

Dynamic modelling of travellers’ social interactions and social learning

Social interaction and social learning are likely to be influential factors in the travel choices made by individuals and the dynamics of these choices. This study aims to understand the influence these social aspects have on travellers’ decision making and behaviour. Furthermore, this research seeks to find out the possibility of utilising this understanding to enhance policies on behavioural change. Social interactions, which may due to an interdependent situation between travellers, social information about other travellers’ behaviour and communication between travellers enable social learning and social influence processes between travellers. Social psychology theories have been used to provide the underlying framework for the study as well as the methods for analysing the data using the individual and social learning models. This study utilises a laboratory experiment to capture the role of social interactions and social learning in the dynamics of travellers’ decision making over time. A major finding of the laboratory experiment is that social interaction and social learning influence individuals’ behaviour. However providing more social information makes people behave in a less cooperative way and be more unstable in making choices. It also influences more people to make contrarian than direct responses. Analyses reveal that people learn individually from their previous experience and socially from other people. It is revealed that confirmation (keeping previous behaviour when observed individuals also chose the same choice) and conformity (following the choice of the majority) are exhibited whenever individuals have access to social information, and therefore could be incorporated into models of travel choice. These findings elicit some behavioural, policy and methodological insights.

Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments

In recent “learning to forecast” experiments (Hommes et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations, and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea is evolutionary selection among heterogeneous expectation rules, driven by their relative performance. The out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting and aggregate price behavior. (JEL C53, C91, D83, D84, G12)

A Cross-Level Test of Empowerment and Process Influences on Members’ Informal Learning and Team Commitment

Team effectiveness can be gauged not only by team-level outcomes but also in terms of influences on individual members. The authors present a two-sample cross-level model of individual informal learning and team commitment, as influenced by team-level processes and empowerment. In the first sample, they examine the impact of team-level constructs on individual outcomes, using 575 individuals from 80 teams in an international Fortune 500 manufacturing firm. Using a sample of 67 teams and 271 individual service engineers from a separate Fortune 500 business solutions organization, the authors further advance their model using a pre–post intervention design. Informal learning and commitment are shown to be positively impacted by team-level empowerment and processes, over and above individual predispositions and perceptions in both samples. In addition, changes in team empowerment and processes attributable to the intervention account for additional criterion variance. Implications are discussed in terms of both research and practice.

Harnessing collective knowledge inherent in tag clouds

AbstractTagging systems represent the conceptual knowledge of a community. We experimentally tested whether people harness this collective knowledge when navigating through the Web. As a within‐factor we manipulated people's prior knowledge (no knowledge vs. prior knowledge that was congruent/incongruent to the collective knowledge inherent in the tags). As between‐factor we manipulated whether people had tag clouds available or not. In line with the Information Foraging Theory and with the Co‐Evolution Model of individual learning and collective knowledge building, we found that people's prior knowledge and tag clouds influenced their navigation. Tags which were congruent with people's prior knowledge had a higher probability of being selected. A knowledge test showed that participants adapted their individual conceptual knowledge to the collective knowledge. This incidental learning shows that people harness collective knowledge just by navigation with tag clouds.