A Seasonal Comparison of the Effectiveness of Parent Engagement on Student Literacy Achievement

Parent engagement is often promoted as a remedy for reducing achievement gaps between students from high socio-economic and low socio-economic backgrounds. However, researchers have found mixed results when examining parent engagement and student outcomes. Drawing on a study investigating the effectiveness of summer literacy camps offered by schools in Ontario, I compare the influence of parent engagement on two outcomes: (1) spring snapshot of cumulative learning, and (2) summer literacy growth/loss. In considering summer learning in regression analysis, I aim to investigate the effect of parent engagement without the influence of schools during the academic year. Out of 14 parent engagement measures, I find only three (parents’ aspirations, home resources, discussions of school with children) are positive predictors of spring literacy outcomes and that none predict summer literacy growth/loss. Family socio-economic status remains a powerful predictor of achievement for both outcomes. I interpret my findings within three proposed mechanisms of parent engagement: cultivation ethic, realist reaction, and expressive logic. Keywords: parent engagement, literacy achievement, socio-economic status, inequality, summer learning, summer literacy camp(s)

A Metamodel and Framework for Artificial General Intelligence From Theory to Practice

This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning/symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski’s general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition.

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available.