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Designing for change: mash-up personal learning environments
Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down.
The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed.
In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services.
Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems
A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data
Neural-symbolic approaches to machine learning incorporate the advantages
from both connectionist and symbolic methods. Typically, these models employ a
first module based on a neural architecture to extract features from complex
data. Then, these features are processed as symbols by a symbolic engine that
provides reasoning, concept structures, composability, better generalization
and out-of-distribution learning among other possibilities. However, neural
approaches to the grounding of symbols in sensory data, albeit powerful, still
require heavy training and tedious labeling for the most part. This paper
presents a new symbolic-only method for the generation of hierarchical concept
structures from complex spatial sensory data. The approach is based on
Bateson's notion of difference as the key to the genesis of an idea or a
concept. Following his suggestion, the model extracts atomic features from raw
data by computing elemental sequential comparisons in a stream of multivariate
numerical values. Higher-level constructs are built from these features by
subjecting them to further comparisons in a recursive process. At any stage in
the recursion, a concept structure may be obtained from these constructs and
features by means of Formal Concept Analysis. Results show that the model is
able to produce fairly rich yet human-readable conceptual representations
without training. Additionally, the concept structures obtained through the
model (i) present high composability, which potentially enables the generation
of 'unseen' concepts, (ii) allow formal reasoning, and (iii) have inherent
abilities for generalization and out-of-distribution learning. Consequently,
this method may offer an interesting angle to current neural-symbolic research.
Future work is required to develop a training methodology so that the model can
be tested against a larger dataset
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment
This paper presents a new approach to the solution of Probabilistic Risk
Assessment (PRA) models using the combination of Reinforcement Learning (RL)
and Graph Neural Networks (GNNs). The paper introduces and demonstrates the
concept using one of the most popular PRA models - Fault Trees. This paper's
original idea is to apply RL algorithms to solve a PRA model represented with a
graph model. Given enough training data, or through RL, such an approach helps
train generic PRA solvers that can optimize and partially substitute classical
PRA solvers that are based on existing formal methods. Such an approach helps
to solve the problem of the fast-growing complexity of PRA models of modern
technical systems
Students' Modelling in Learning The Concept of Speed
Previous researchs shows that speed is one of the most difficult in the upper grades of primary school. It is because students must take into consideration of two variables; distance and time. Nevertheless, Indonesian students usually learn this concept as a transmission subject and teacher more emphasizes on formal mathematics in which the concept of speed given as distance by time rigorously. A sequence of learning activities with toy cars context was designed based on students' development and Realistic Mathematics Education (RME) principles which are guided reinvention, didactical phenomenology and emergent modelling. Using their own models, students are able to explain a proportion among distance and time in speed as well the relationship of it. Keywords: The concept of speed, design research, Toy cars, context, ratio tables' mode
Modeling the Psychology of Consumer and Firm Behavior with Behavioral Economics
Marketing is an applied science that tries to explain and influence how firms and
consumers actually behave in markets. Marketing models are usually applications of
economic theories. These theories are general and produce precise predictions, but they
rely on strong assumptions of rationality of consumers and firms. Theories based on
rationality limits could prove similarly general and precise, while grounding theories in
psychological plausibility and explaining facts which are puzzles for the standard
approach.
Behavioral economics explores the implications of limits of rationality. The goal is to
make economic theories more plausible while maintaining formal power and accurate
prediction of field data. This review focuses selectively on six types of models used in
behavioral economics that can be applied to marketing.
Three of the models generalize consumer preference to allow (1) sensitivity to reference
points (and loss-aversion); (2) social preferences toward outcomes of others; and (3)
preference for instant gratification (quasi-hyperbolic discounting). The three models are
applied to industrial channel bargaining, salesforce compensation, and pricing of virtuous
goods such as gym memberships. The other three models generalize the concept of gametheoretic
equilibrium, allowing decision makers to make mistakes (quantal response
equilibrium), encounter limits on the depth of strategic thinking (cognitive hierarchy),
and equilibrate by learning from feedback (self-tuning EWA). These are applied to
marketing strategy problems involving differentiated products, competitive entry into
large and small markets, and low-price guarantees.
The main goal of this selected review is to encourage marketing researchers of all kinds
to apply these tools to marketing. Understanding the models and applying them is a
technical challenge for marketing modelers, which also requires thoughtful input from
psychologists studying details of consumer behavior. As a result, models like these could
create a common language for modelers who prize formality and psychologists who prize
realism
DESAIN PEMBELAJARAN MATEMATIKA DENGAN KONTEKS PERMAINAN KULI BIA PADA MATERI FAKTOR PERSEKUTUAN TERBESAR DAN KELIPATAN PERSEKUTUAN TERKECIL
The learning process of the concept of greatest and Least which are the prerequisites of the greatest common divisor and the least common multiple in class IV SD Inpres 2 Waipo is carried out with the teaching paradigm that is, using T tables, multiples, and not using student thought conjectors in find concepts or material. This underlies researchers to design learning designs using Realistic Mathematics Education (RME) with the context of the kuli bia game as a starting point. The purpose of this study is to produce a learning trajectory and determine the impact of the use of learning designs on the mathematical knowledge construction process. Design research was chosen to achieve this goal which consists of three stages: preliminary design, teaching experiment, retrospective analysis. The results of the study found that the kuli bia game provides an important role in supporting learning and increases learning motivation, able to understand the material based on the learning trajectories generated, provides a variety of strategies in solving problems both at the situational stage, models of, models for and formal stages, the implementation of RME with the context of the kuli bia game on the material is running optimally, the response of students and teachers is positive and effective management of learning in the classroom
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