Thesis: M.S., Massachusetts Institute of Technology, Department of Food Technology, 1960 / Includes bibliographical references (leaves 46-51). / by Kyoung Sook Kim. / M.S. / M.S. Massachusetts Institute of Technology, Department of Food Technology

Institutional inhibiting factors, and lack of motivation and support at the University of Eduardo Mondlane (UEM) result in lecturers not teaching using technology-augmented approaches. This research study investigates how a faculty-based EdTech-UEM professional development model could potentially foster Communities of Practice (CoPs) for technology-augmented pedagogical practices in UEM faculties. One way of addressing the technology-augmented pedagogical practices is to understand motivations, pedagogical practices, support and barriers of lecturers to adopt them. Lecturers (innovators and adopters: early adopters and late majority adopters) who participated in the four workshops on EdTech professional development, reported instances of innovators exhibiting peer leadership in group-learning activities during the workshops. The CoPs theoretical framework was used to investigate social learning and support among the innovators and adopters. After the workshops, which coincided with the beginning of the pandemic, 18 lecturer participants of the workshops were purposefully selected, and semi-structured interviews were conducted with the goal of better understanding the support needs among lecturers (innovators and adopters) after the workshops as well as discussing the best ways to overcome potential barriers and challenges in technology-augmented pedagogical practices and provide a conducive environment for the emergence of CoPs in the adoption of technology-augmented pedagogical practices. The workshops were before the pandemic and lecturers implemented what they learnt during the pandemic. It was serendipitous as the workshops helped them to prepare for ERT even it was not the aim of the initiative that completed before the pandemic started. The interviews were recorded and transcribed, and the empirical data was analysed through an interpretive approach. The interpretive method looks at how people make sense of their experiences, as well as how they construct and attribute meaning to their worlds. The result of the interpretive analysis was based on the description of qualitative themes formed and related from each other in multiple case studies of both innovators and adopters. The anonymity of their responses was guaranteed.

Thesis: M.S., Massachusetts Institute of Technology, Department of Food Technology, 1960 / Includes bibliographical references (leaves 97-99). / by Arthur E. Humphrey. / M.S. / M.S. Massachusetts Institute of Technology, Department of Food Technology

No description available.

As perceived by Boston computer teachers, to what degree is the Computer Literacy program in the public schools of Boston serving its students and faculties? To answer this question, the goal of this research was to make an assessment of the perceptions of Boston computer teachers concerning the teaching of Computer Literacy in their schools. A review of the literature and the discussions held in connection with a pilot study showed that a reasonable set of questions could be developed to provide some definitive answers. These answers expressed not only what individual teachers do encounter on a day-to-day basis, but also what they believed should be occurring in their classrooms. A questionnaire was designed with three "Areas of Interest" to obtain data. The three "Areas of Interest" were called "Curriculum," "Facilities," and "Policies." After the pilot study-group arrived at consensus, a reliability test was conducted on the instrument. Then the questionnaire and accompanying material was mailed to every certified computer teacher listed by the City of Boston School Department. With extensive follow-up procedures, the return rate reached 78%. The computer facilities of the University of Massachusetts (Harbor Campus), using a statistics management program called "Statistical Package for the Social Sciences (Version X)," treated the received data. The questionnaire asked for two responses to each question: one for current practice ("Does"), and the other for desired conditions ("Should"). For each question, "SSPSx" generated Means, Mean Discrepancies, Standard Deviations, and also made ANOVA comparisons between subgroups which related to school levels taught and to years of experience in teaching Computer Literacy. The data from Boston Computer Literacy teachers ranked the three "Areas of Interest," with "Curriculum" most favorable and "Policies" least favorable. Middle school teachers with longer experience were most content with the current conditions. Those teachers with an average (four to six years) length of experience appeared to be the most disturbed, regardless of the school level at which they worked.

The economies of most Sub-Saharan African countries are linked to the onset, reliability and performance of seasonal rainfall. Failure of seasonal rains may signal food deficits or worse. Farmers, water conservationists and government bodies responsible for food security, all have an interest in seasonal rainfall: onset, approximate dates for start of the season and probabilities for early, normal or late onset of rains. This knowledge enables them make crucial decisions as to the choice of crops, planting dates, management of dams, pasture and hydro-electric dams. In this thesis, daily rainfall data for 29 rainfall stations in Botswana for the years 1971 - 2004 was analyzed to determine Start-of-Season (505)/ Onset of summer rainfall. We used Principal Component Analysis to determine rainfall homogeneous zones in Botswana. Basically three regions were identified for October, November December (OND) rainfall months. Rainfall values in representative stations in each zone (Northern, Central and South-Eastern and Western regions) were correlated with Sea Surface Temperatures (SSTs) in global oceans to determine ocean regions that correlate well with Botswana rainfall. The onset dates were grouped into false, early, normal, late and failed onsets. Monthly rainfall and Rainfall Onsets for selected 14 rainfall stations and ten other weather parameters, (that include SSTs, Sea Level Pressures (SLPs) and climate indices) were placed in a spreadsheet. Emergent Situation Awareness (ESA) for dynamic Bayesian networks (DBN) was used to analyze this data. The ESA for DBN models temporal dependencies among the weather parameters and climate indices using Direct Acyclic Graphs (DAG). This innovative DBN technology, ESA, reveals more detailed information from complex models. It reveals what is currently happening over time in a domain of interest. Each of the parameters and climate indices revealed varying degrees of beliefs for early, normal, late or failed rainfall onsets in Botswana. Some of the parameters which showed higher degrees of beliefs are promising signals to the onset of summer rains.

This thesis presents Axone, a system that enables decentralized collaborative creation of digital products through interconnected digital content blocks. Axone provides the provenance of a digital product by storing its history since creation in an immutable Directed Acyclic Graph (DAG) data structure. This history comprises digital content blocks used in its creation, including how they referenced each other in the development of the final digital product. Through referencing, credit attribution is achieved and royalty fees due to the referenced content block are recorded and enforced. Content creators can concurrently work on a succeeding content block to produce various versions of unique digital products from the same original content block. Axone focuses on written work enabling different authors to contribute to a book (the digital product) in the form of chapters (digital content blocks), until its completion. Axone uses blockchain technology and web monetization to provide provenance for each chapter and to stream payments to authors.

Thesis: B.S., Massachusetts Institute of Technology, Department of Food Technology, 1951 / Bibliography: leaves 41-43. / by Aaron L. Brody. / B.S. / B.S. Massachusetts Institute of Technology, Department of Food Technology

Interdisciplinary research has demonstrated that learning and problem solving with advanced learning technologies (ALTs) such as intelligent tutoring systems, simulations, hypermedia, serious games, and virtual reality can promote and foster the development of 21st century skills (e.g., collaboration, problem solving, self-regulated learning) by measuring and using the interactions between cognitive, affective, metacognitive, motivational, and social (CAMMS) processes. Interdisciplinary researchers focused on self-regulated learning (SRL) have developed several theoretical models which model students' CAMMS processes and their learning behaviors. However, when empirically testing these models, researchers face complicated methodological decisions around modeling, measuring, processing, and analyzing student data. Many of these questions come from examining the interactions of the various processes in relation to overall learning instead of the isolated examination of each process independent of one another. This is especially true when looking across CAMMS (e.g., metacognitive regulation and motivational engagement) and not just within a single CAMMS process (e.g., metacognitive monitoring and control). For instance, metacognition and engagement are often discussed informally in conjunction with one another, however, many models of SRL provide a cursory mention of this relationship at best, if at all. Therefore, comprehensive models of both metacognition and engagement are needed to define future work within this field. Critically, this modeling needs to be specific about the component operationalizations and interactions, the dynamics of the components, and the conditions by which metacognition and engagement may interact. This may be accomplished by utilizing a combination of online dynamic multimodal data captured during learning, reasoning, and problem solving (revealing the what, when, and for how long), and traditional offline self-reports (revealing the why) as we measure, model, and (in the future) simulate learners and their metacognitive and engagement processes.

No description available.