Kinds of loops implemented with messages in Scratch and the SOLO Taxonomy

2019 ◽  
Author(s):  
Theodoros Karvounidis ◽  
Anastasios Ladias ◽  
Demitrios Ladias ◽  
Christos Douligeris
Keyword(s):  
Solo Taxonomy

ANALYSIS OF NARRATIVE MATHEMATICAL QUESTIONS ON TEXTBOOKS BASED ON SOLO TAXONOMY AND MATHEMATICAL POWER

2017 ◽  
Vol 5 (3) ◽  
pp. 299
Author(s):  
Imam Kusmaryono
Keyword(s):  
Problem Solving ◽  
Grade Level ◽  
Mathematical Representation ◽  
Structural Level ◽  
Mathematics Textbooks ◽  
Solo Taxonomy ◽  
Research Procedure ◽  
Quantitative Descriptive ◽  
The Ideal

This study aimed to identify the proportion of diversity and suitability of narrative mathematical questions with SOLO taxonomy level and mathematical power ability. The research was conducted through quantitative descriptive. Sources of data in the forms of narrations contained in mathematics textbooks. The research procedure was conducted by making the classification and determining the percentage of the narrations based on the compatibility of SOLO taxonomy and the mathematical power ability. The results showed that, the narrative mathematical questions with uni-structural level are of 7.5%, multi-structural of 33.8%, relational of 46.6% and extended abstract of 12.1 %. In terms of compatibility of the narrative  questions were able to measure 23% reasoning aspect, 18% problem solving, 8.3% connection, 28% communication and 22.6% mathematical representation. In general, mathematics textbooks as the object of research should be revised, since they have not yet achieved the ideal alignment between SOLO taxonomy based on grade level and the objective of learning develop mathematical power

scholarly journals ANALISIS RESPON MAHASISWAPGSD DALAM MENYELESAIKAN SOAL IPABERDASARKAN STRUCTURE OF OBSERVED LEARNING OUTCOME TAXONOMY DITINJAU DARI MOTIVASI BELAJAR

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Siti Fatimah
Keyword(s):  
Qualitative Research ◽  
Problem Solving ◽  
Research Method ◽  
Learning Outcome ◽  
Data Sources ◽  
Motivation To Learn ◽  
Solo Taxonomy ◽  
High Category ◽  
Abstract Level

SOLO Taxonomy is one of the frameworks used in analyzing the quality of responses in problem solving. This research aim to analyze the student’s responses level in doing problem solving item based on SOLO Taxonomy overviewed from motivation to learn. The research method used in this study was a qualitative research with the data sources of this study were 75 students period 2016/2017. The results of the study showed that student’s responses in doing problem solving wave concept at prestructural level is 62,17%, Unistructural Level is 27,63%, Multistructural level is 58,22%, Relasional level is 48,50%, and Extended Abstract level is 37,82%; Motivation to learn’s student high category are better at responding to answer item based SOLO Taxonomy than motivation to learn’s students low category.Keywords: Student’s reponses; SOLO Taxonomy; Motivation to Learn

scholarly journals Profil Kemampuan Koneksi Matematis Mahasiswa Calon Guru dalam Menyelesaikan Masalah Matematika Berdasarkan Taksonomi Solo

2020 ◽  
Vol 13 (1) ◽  
pp. 17-33
Author(s):  
Risma Firda Diana
Keyword(s):  
Mathematical Problem ◽  
High Ability ◽  
Education Students ◽  
Solo Taxonomy ◽  
Interview Guide ◽  
High Ability Students ◽  
Teacher Education Students ◽  
Descriptive Method ◽  
Mathematical Connection ◽  
Research Show

This research aims to describe profil of preservice teacher’s mathematical connection ability in solving mathematical problem based on SOLO taxonomy. The approach of this research is qualitative with descriptive method. Respondent in this research is 29 Madrasah Ibtidaiyah Teacher Education students STAI Al-Fithrah Surabaya. This research uses mathematical connection test and  interview guide to collect data. The result of this research show that the  average mathematical connection ability of high-ability students is 85% with a very good category and that is at the level of multistructural to relational, the  average mathematical connection ability of medium-ability students is 50,5% with enough category and that is at the level of unistructural to multistructural, and  the  average mathematical connection ability of low-ability students is 16% with a very low category and that is at the level of pre-structural until unistructural level.

scholarly journals Exploring Student Sustainability Knowledge using the Structure of Observed Learning Outcomes (SOLO) Taxonomy

2020 ◽  
Author(s):  
Mary Watson ◽  
Joshua Pelkey ◽  
Michael Rodgers ◽  
Caroline Noyes
Keyword(s):  
Learning Outcomes ◽  
Solo Taxonomy

scholarly journals EFFECTIVELY ASSESSING PROFESSIONAL ENGINEERING SKILLS

2011 ◽  
Author(s):  
David S. Strong ◽  
Sue Fostaty Young
Keyword(s):  
Faculty Members ◽  
Learning Activities ◽  
First Year ◽  
Solo Taxonomy ◽  
Adoption And Implementation ◽  
Cognitive Transformation ◽  
Teachers And Students ◽  
Learning As A Process ◽  
Core Course ◽  
Ice Model

This paper outlines the assessment dilemmas and challenges that were experienced by faculty members and students alike during initial iterations of APSC 190 (a first-year, professional engineering skills core course in the Faculty of Applied Science at Queen’s University) and how the adoption and implementation of the ICE model of assessment [1], [2], [3] served to address those challenges. ICE, an acronym for Ideas, Connections and Extensions is based on cognitive/transformation theories of learning similar to those put forth by Biggs’ and Collis’ SOLO taxonomy[4], and describes learning as a process of growth from novice toward expert. Unlike SOLO, ICE was intentionally designed for use in the classroom by teachers and students. The simplicity of the model increases its utility and portability to a host of learning activities and furnishes an accessible vocabulary and framework to facilitate communication about expectations for learning. The paper includes an overview of the ICE model, suggestions for implementation and the effects and limitations of the model for use in professional skills courses. Current-use examples are provided that illustrate the model’s utility and its implications for shaping student learning.

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