The Design of Learning Material for Poor Comprehenders: Lessons Learnt from Experts

TERENCE is an FP7 ICT European project that is developing an adaptive learning system for poor comprehenders and their educators. The learning material is made of stories and smart games for stimulating reading comprehension. The design of stories and smart games is also based on data collected from experts for the analysis of the context of use of the system, and is incrementally revised via evaluations of prototypes of stories and games, with domain experts of text comprehension or education as participants. In particular, since smart games are semi-automatically generated via artificial intelligence technologies, they contain mistakes that have to be fixed by experts of pedagogy before the games are given to learners. In this paper we focus on the design and evaluations of the TERENCE stories and smart games for poor comprehenders via lessons learnt with domain experts.

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Design Smart Games with requirements, generate them with a Click, and revise them with a GUIs

ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL ◽

10.14201/adcaij20121315568 ◽

2013 ◽

Vol 1 (3) ◽

pp. 55-68 ◽

Cited By ~ 1

Author(s):

Vincenza Cofini ◽

Fernando De La Prieta ◽

Tania Di Mascio ◽

Rosella Gennari ◽

Pierpaolo Vittorini

Keyword(s):

Adaptive Learning ◽

Learning System ◽

Story Comprehension ◽

Learning Material ◽

Generation Process ◽

Inference Making ◽

Automated Generation ◽

Poor Comprehenders ◽

Adaptive Learning System

TERENCE is an FP7 ICT European project that is developing an adaptive learning system for supporting poor comprehenders and their educators. Its learning material are books of stories and games. The games are specialised into smart games, which stimulate inference-making for story comprehension, and relaxing games, which stimulate visual perception and not story comprehension. The paper focuses on smart games. It first describes the TERENCE system architecture, thus delves into the design of smart games starting from the requirements and their automated generation, by highlighting the role of the reasoning module therein. Finally, it outlines the manual revision of the generated smart games, and ends with short conclusions about the planned improvements on the automated generation process.

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SCIENCE EDUCATION ADAPTIVE LEARNING SYSTEM AS A COMPUTER-BASED SCIENCE LEARNING WITH LEARNING STYLE VARIATIONS

Journal of Baltic Science Education ◽

10.33225/jbse/18.17.711 ◽

2018 ◽

Vol 17 (4) ◽

pp. 711-727 ◽

Cited By ~ 1

Author(s):

Zulfiani Zulfiani ◽

Iwan Permana Suwarna ◽

Sujiyo Miranto

Keyword(s):

Learning Styles ◽

Learning Style ◽

Adaptive Learning ◽

Science Learning ◽

Learning System ◽

Learning Material ◽

Learning Approaches ◽

Slow Learners ◽

Computer Based ◽

Adaptive Learning System

Students with their different learning styles also have their own different learning approaches, and teachers cannot simultaneously facilitate them all. Teachers’ limitation in serving all students’ learning styles can be anticipated by the use of computer-based instructions. This research aims to develop ScEd-Adaptive Learning System (ScEd-ASL) as a computer-based science learning media by accommodating students’ learning style variations. The research method used is a mixed method at junior high schools in Tangerang Selatan. The final product of the research is a special learning media appropriate to students’ visual, aural, read/write and kinesthetic learning styles. The uniqueness of the media is its form of integrated science materials, accommodating fast and slow learners, and appropriate to their learning styles. ScEd-Adaptive Learning System as a developed computer-based science learning media was declared as good and valid by four media experts and five learning material experts. ScEd-ALS for kinesthetic style has a high effectivity to improve students learning mastery (100%), consecutively aural (63%), read/write (55%), and visual (20%). This media development can be continued with the Android version or iOS to make it more operationally practical. Keywords: adaptive learning system, science learning media, computer-based instruction, learning style.

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Modelling an Adaptive Learning System Using Artificial Intelligence

Webology ◽

10.14704/web/v19i1/web19001 ◽

2021 ◽

Vol 19 (1) ◽

pp. 01-18

Author(s):

Hayder Rahm Dakheel AL-Fayyadh ◽

Salam Abdulabbas Ganim Ali ◽

Dr. Basim Abood

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Adaptive Learning ◽

Back Propagation ◽

Learning System ◽

Spiking Neural Networks ◽

Domain Specific Language ◽

Domain Specific ◽

Adaptive Learning System ◽

Artificial Neural

The goal of this paper is to use artificial intelligence to build and evaluate an adaptive learning system where we adopt the basic approaches of spiking neural networks as well as artificial neural networks. Spiking neural networks receive increasing attention due to their advantages over traditional artificial neural networks. They have proven to be energy efficient, biological plausible, and up to 105 times faster if they are simulated on analogue traditional learning systems. Artificial neural network libraries use computational graphs as a pervasive representation, however, spiking models remain heterogeneous and difficult to train. Using the artificial intelligence deductive method, the paper posits two hypotheses that examines whether 1) there exists a common representation for both neural networks paradigms for tutorial mentoring, and whether 2) spiking and non-spiking models can learn a simple recognition task for learning activities for adaptive learning. The first hypothesis is confirmed by specifying and implementing a domain-specific language that generates semantically similar spiking and non-spiking neural networks for tutorial mentoring. Through three classification experiments, the second hypothesis is shown to hold for non-spiking models, but cannot be proven for the spiking models. The paper contributes three findings: 1) a domain-specific language for modelling neural network topologies in adaptive tutorial mentoring for students, 2) a preliminary model for generalizable learning through back-propagation in spiking neural networks for learning activities for students also represented in results section, and 3) a method for transferring optimised non-spiking parameters to spiking neural networks has also been developed for adaptive learning system. The latter contribution is promising because the vast machine learning literature can spill-over to the emerging field of spiking neural networks and adaptive learning computing. Future work includes improving the back-propagation model, exploring time-dependent models for learning, and adding support for adaptive learning systems.

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