In September 1968 the University of Hawaii began work on a research program to investigate the use of radio communications for computer-computer and console-computer links. In this report we describe a remote-access computer system---THE ALOHA SYSTEM---under development as part of that research program and discuss some advantages of radio communications over conventional wire communications for interactive users of a large computer system. Although THE ALOHA SYSTEM research program is composed of a large number of research projects, in this report we shall be concerned primarily with a novel form of random-access radio communications developed for use within THE ALOHA SYSTEM.

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Dartmouth Time-Sharing. Development of the System by a Team of Faculty and Undergraduates Is Described

THE ALOHA SYSTEM: another alternative for computer communications

E-learning systems have witnessed a usage and research increase in the past decade. This article presents the e-learning concepts ecosystem. It summarizes the various scopes on e-learning studies. Here [the authors] propose an e-learning theoretical framework. This theory framework is based upon three principal dimensions: users, technology, and services related to e-learning. This article presents an in-depth literature review on those dimensions. The article first presents the related concepts of computer use in learning across time, revealing the emergence of new trends on e-learning. The theoretical framework is a contribution for guiding e-learning studies. The article classifies the stakeholder groups and their relationship with e-learning systems. The framework shows a typology of e-learning systems' services. This theoretical approach integrates learning strategies, technologies and stakeholders.

/pdf/an-e-learning-theoretical-framework-m74wa2eg73.pdf

An e-Learning Theoretical Framework.

What knowledge does learning programming require? Prior work has focused on theorizing program writing and problem solving skills. We examine program comprehension and propose a formal theory of program tracing knowledge based on control flow paths through an interpreter program's source code. Because novices cannot understand the interpreter's programming language notation, we transform it into causal relationships from code tokens to instructions to machine state changes. To teach this knowledge, we propose a comprehension-first pedagogy based on causal inference, by showing, explaining, and assessing each path by stepping through concrete examples within many example programs. To assess this pedagogy, we built PLTutor, a tutorial system with a fixed curriculum of example programs. We evaluate learning gains among self-selected CS1 students using a block randomized lab study comparing PLTutor with Codecademy, a writing tutorial. In our small study, we find some evidence of improved learning gains on the SCS1, with average learning gains of PLTutor 60% higher than Codecademy (gain of 3.89 vs. 2.42 out of 27 questions). These gains strongly predicted midterms (R2=.64) only for PLTutor participants, whose grades showed less variation and no failures.

https://dl.acm.org/doi/pdf/10.1145/3105726.3106178

Comprehension First: Evaluating a Novel Pedagogy and Tutoring System for Program Tracing in CS1

T he dramatic influx of technology into America's schools since the 1990s prompts the question of technology's role as a lever for policy. We begin this chapter with a brief sketch of alternative perspectives on the ways in which technology can support education policy and practice. We will suggest that the connection between technology and policy is looser than that between policy and the other mechanisms described in this volume (such as standards or state assessments) and that technology's potential for profound influences on instruction is yet to be realized. After the introduction to alternative ways in which policymakers have viewed technology's role, we focus on emerging areas of classroom use of technology where prospects for significant changes in teaching and learning seem strongest. Our selection of particular technology uses for more extended treatment reflects our choice of teaching and learning at the classroom level as our central focus.1 In an education system as decentralized as that of the United States, teachers have considerable latitude-even in these days of increased accountability-in interpreting and implementing policies developed at higher levels of the education system. The view of instruction underlying our thinking concerning the policy-technology connection is congruent with Cohen and Ball's (1999) description of instructional capacity as the product of complex interactions among teachers, students, and instructional content. In this view, instructional materials or regimens are not fixed entities with entirely predictable effects. Rather, "teachers mediate instruction: their interpretation of educational materials affects curriculum potential and use, and their understanding of students affects students' opportunities to learn" (p. 4). Students, in turn, respond to teachers and materials in ways that influence subsequent teacher actions. Cohen and Ball caution policymakers against assuming that addressing a single aspect of this complex system (even an aspect that is as strong a policy driver as curriculum materials or assessments) can in fact have the intended effect on the system as

background

Chapter 5: Technology’s Contribution to Teaching and Policy: Efficiency, Standardization, or Transformation?:

Imagine a modern personal computer. It has a direct-manipulat ion, WYSIWYG user interface, and programs for drawing, painting, writing, and other tasks. It is connected to a LAN with email, database, print, and other services. The LAN may be connected to other LANs, forming an internet, and it may be connected to a wide-area network like the Internet. That also describes the Xerox Palo Alto Research Center (PARC) nearly r20 years ago [24]. Today's personal = computers are designed and sold by Apple, IBM, and others, but rein searchers had developed the ideas and techniques they use by the mido 1970s. This column discusses nine of z those early ideas, the people who had o them, and the labs where they m worked. 1 We begin with the observation that an information process~, ing machine was needed to augment intellect. z Augmenting Human Intellect * In an Atlantic Monthly article published jus t af ter World War II, Vannevar Bush, a p rominen t researcher at MIT, stated a problem: scientists were increasingly unable to keep up with professional information. They could not f ind or read everything in their fields, and what they did read (and write) they often forgot or misfiled because of inflexible indexing systems. Having stated a problem, Bush postulated a machine to solve it. He called it \"memex \" because it was to extend the powers of human memory and association:

Before the Altair: the history of personal computing

Systematic enterprise that builds and organizes knowledge