2016 IEEE Global Engineering Education Conference (EDUCON) 2016
DOI: 10.1109/educon.2016.7474524
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Teaching programming basics for first year non-IT students

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“…Finally, two introductory machine learning techniques are discussed for data processing. Active learning increases student success in STEM [6] and leads to increased student retention and engagement [7][8][9][10][11]. We use Jupyter notebooks [45] to implement active learning.…”
Section: Overview Of the New Curriculummentioning
confidence: 99%
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“…Finally, two introductory machine learning techniques are discussed for data processing. Active learning increases student success in STEM [6] and leads to increased student retention and engagement [7][8][9][10][11]. We use Jupyter notebooks [45] to implement active learning.…”
Section: Overview Of the New Curriculummentioning
confidence: 99%
“…We are building on prior work by others using active learning [6][7][8][9][10][11], PjBL [12][13][14][15][16][17], worked examples [18][19][20], Jupyter notebooks [21,22], agile software development methods [23][24][25], as well as existing IoT course materials [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. However, the existing mechatronics course materials with IoT tend to target Electrical Engineering (EE) and Computer Science (CS) students and the creation of underlying IoT technologies, especially low-level software.…”
Section: Introductionmentioning
confidence: 99%