Modeling the optional infinite stage in MOSAIC: A generalization to Dutch

This paper presents a model of a stage in children’s language development known as the optional infinitive stage. The model was originally developed for English, where it was shown to provide a good account of several phenomena. The model, which uses a discrimination network, analyzes the distribution of words in the input, and derives word classes from them by linking words that are used in a similar context. While the earlier version of the model is sensitive only to characteristics of phrases that follow target words, the present version also takes preceding input into consideration. Also, the present version uses a probabilistic rather than a deterministic learning mechanism. Generalisation of the model to Dutch is considered a strong test of the model, since Dutch displays the optional infinitive phenomenon, while its syntax differs substantially from that of English. The model was presented with child-directed input from two Dutch mothers, and its output was compared to that of the respective children. Despite the fact that the model was developed for a different language, it captures the optional infinitive phenomenon in Dutch as it does in English, while showing sensitivity to Dutch syntax. These results suggest that a simple distributional analyzer can capture the regularities of different languages despite the apparent differences in their syntax

Development and evaluation of a web-based learning system based on learning object design and generative learning to improve higher-order thinking skills and learning

This research aims to design, develop and evaluate the effectiveness of a Webbased learning system prototype called Generative Object Oriented Design (GOOD) learning system. Result from the preliminary study conducted showed most of the students were at lower order thinking skills (LOTS) compared to higher order thinking skills (HOTS) based on Bloom’s Taxonomy. Based on such concern, GOOD learning system was designed and developed based on learning object design and generative learning to improve HOTS and learning. A conceptual model design of GOOD learning system, called Generative Learning Object Organizer and Thinking Tasks (GLOOTT) model, has been proposed from the theoretical framework of this research. The topic selected for this research was Computer System (CS) which focused on the hardware concepts from the first year Diploma of Computer Science subjects. GOOD learning system acts as a mindtool to improve HOTS and learning in CS. A pre-experimental research design of one group pretest and posttest was used in this research. The samples of this research were 30 students and 12 lecturers. Data was collected from the pretest, posttest, portfolio, interview and Web-based learning system evaluation form. The paired-samples T test analysis was used to analyze the achievement of the pretest and posttest and the result showed that there was significance difference between the mean scores of pretest and posttest at the significant level a = 0.05 (p=0.000). In addition, the paired-samples T test analysis of the cognitive operations from Bloom’s Taxonomy showed that there was significance difference for each of the cognitive operation of the students before and after using GOOD learning system. Results from the study showed improvement of HOTS and learning among the students. Besides, analysis of portfolio showed that the students engaged HOTS during the use of the system. Most of the students and lecturers gave positive comments about the effectiveness of the system in improving HOTS and learning in CS. From the findings in this research, GOOD learning system has the potential to improve students’ HOTS and learning

Rich environments for active learning in action: Problem‐based learning

Rich Environments for Active Learning (REALs) are comprehensive instructional systems that are consistent with constructivist theories. They promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher‐order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning‐to‐learn within authentic contexts using realistic tasks and performances. Problem‐Based Learning (PBL) is an instructional methodology that can be used to create REALs. PBL's student‐centred approach engages students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, PBL and REALs are a response to teacher‐centred educational practices that promote the development of inert knowledge, such as conventional teacher‐to‐student knowledge dissemination activities. In this article, we compare existing assumptions underlying teacher‐directed educational practice with new assumptions that promote problem solving and higher‐level thinking by putting students at the centre of learning activities. We also examine the theoretical foundation that supports these new assumptions and the need for REALs. Finally, we describe each REAL characteristic and provide supporting examples of REALs in action using PB

Knowledge transformers : a link between learning and creativity

The purpose of this paper is to investigate whether knowledge transformers which are featured in the learning process, are also present in the creative process. This is achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers

Modelling the development of Dutch Optional Infinitives in MOSAIC.

This paper describes a computational model which simulates the change in the use of optional infinitives that is evident in children learning Dutch as their first language. The model, developed within the framework of MOSAIC, takes naturalistic, child directed speech as its input, and analyses the distributional regularities present in the input. It slowly learns to generate longer utterances as it sees more input. We show that the developmental characteristics of Dutch children’s speech (with respect to optional infinitives) are a natural consequence of MOSAIC’s learning mechanisms and the gradual increase in the length of the utterances it produces. In contrast with Nativist approaches to syntax acquisition, the present model does not assume large amounts of innate knowledge in the child, and provides a quantitative process account of the development of optional infinitives

Knowledge transformers : a link between learning and creativity

The purpose of this paper is to investigate whether knowledge transformers that are featured in the learning process are also present in the creative process. First, this was achieved by reviewing accounts of inventions and discoveries with the view of explaining them in terms of knowledge transformers. Second, this was achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers