86 research outputs found
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
A Data-Driven Approach to Compare the Syntactic Difficulty of Programming Languages
Educators who teach programming subjects are often wondering “which programming language should I teach first?”. The debate behind this question has a long history and coming up with a definite answer to this question would be farfetched. Nonetheless, several efforts can be identified in the literature wherein pros and cons of mainstream programming languages are examined, analysed, and discussed in view of their potential to facilitate the didactics of programming concepts especially to novice programmers. In line with these efforts, we explore the latter question by comparing the syntactic difficulty of two modern, but fundamentally different, programming languages: Java and Python. To achieve this objective, we introduce a standalone and purely data-driven method which stores the code submissions and clusters the errors occurred under the aid of a custom transition probability matrix. For the evaluation of this model a total of 219,454 submissions, made by 715 first-year undergraduate students, in 259 unique programming exercises were gathered and analysed. The results indicate that Python is an easier-to-grasp programming language and is, therefore, highly recommended as the steppingstone in introductory courses. Besides, the adoption of the described method enables educators to not only identify those students who struggle with coding (syntax-wise) but further paves the pathway for the adoption of personalised and adaptive learning practices
Applied Cognitive Sciences
Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline
A Celebration of West Point Authors
Today we celebrate the 480 works of scholarship produced at the Academy between January and June 2022. The program features speakers whose work centered around civil-military relations.https://digitalcommons.usmalibrary.org/books/1064/thumbnail.jp
How Fast Can We Play Tetris Greedily With Rectangular Pieces?
Consider a variant of Tetris played on a board of width and infinite
height, where the pieces are axis-aligned rectangles of arbitrary integer
dimensions, the pieces can only be moved before letting them drop, and a row
does not disappear once it is full. Suppose we want to follow a greedy
strategy: let each rectangle fall where it will end up the lowest given the
current state of the board. To do so, we want a data structure which can always
suggest a greedy move. In other words, we want a data structure which maintains
a set of rectangles, supports queries which return where to drop the
rectangle, and updates which insert a rectangle dropped at a certain position
and return the height of the highest point in the updated set of rectangles. We
show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on
a board of width , if the OMv conjecture [Henzinger et al., 2015]
is true, then both operations cannot be supported in time
simultaneously. The reduction also implies polynomial bounds from the 3-SUM
conjecture and the APSP conjecture. On the other hand, we show that there is a
data structure supporting both operations in time on
boards of width , matching the lower bound up to a factor.Comment: Correction of typos and other minor correction
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