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Aligning Outcomes, Assessment and Activities

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Characteristics of the Teacher

What beliefs and values does the teacher have about teaching and learning?  What is his/her attitude toward:  the subject? students? What level of knowledge or familiarity does s/he has with this subject?  What are his/her strengths in teaching? 

The following are the teacher’s learning philosophy and the plans adopted for training the students about the subject to ensure both competency-based and outcome-based learning are achieved for every student.

Assessment for/as/of Learning:

  • Formative Assessments (For Learning): Online quizzes and discussion forums within the pre-recorded modules allow for self-assessment and peer feedback.
  • Summative Assessments (Of Learning): Midterm exams and final project assess overall understanding and application of data analysis concepts.
  • Performance- Based Assessments (As Learning): In-class group activities, group projects and presentations allow students to show their skills and receive immediate feedback.
Moving Towards Deeper Learning:

This design actively promotes deeper learning through:

  • Problem-solving activities: Students go beyond memorization by applying data analysis techniques to real-world business scenarios.
  • Collaborative projects: Working in teams encourages critical thinking, communication, and knowledge application.
  • Open-ended questions: Discussions and assignments encourage analysis, synthesis, and evaluation of data.

    The teachers are developers who use python programming for advanced data analytics, website development and product deployment. They have years of experience teaching and coaching students in data analytics across various fields such as healthcare, banking, aviation, agriculture, education etc.

    The teachers are developers who use python programming for advanced data analytics, website development and product deployment. They have years of experience teaching and coaching students in data analytics across various fields such as healthcare, banking, aviation, agriculture, education etc.

    Learning Environment & Situational Factors to Consider

    How many students are in the class?  Is the course primary, secondary, undergraduate, or graduate level?  How long and frequent are the class meetings?  How will the course be delivered: live, online, blended, flipped or in a classroom or lab?  What physical elements of the learning environment will affect the class? What technology, networking and access issues will affect the class?

    1. Average of 35 students per class, Level of Instruction: Advanced Level Curriculum – Post Secondary or High School and Pre-University Educational system. This is equivalent to Year 1 College Education in the United States. Intended Audience: Advanced level Students.
    2. Class meetings are 2 hours each twice a week. 2 other online meetings via zoom. Interaction and collaboration on Google Meet. And tracking of work on Learndash which is the Learning management system.
    3. This approach combines online modules with interactive in-class sessions. Students learn foundational concepts through:
      • pre-recorded lectures and
      • activities outside of class in a real-world scenario like leveraging hospital data, etc.
      • Class time focuses on applying knowledge through discussions, problem-solving activities, and collaborative projects.
      • Exploring practice with python platforms – this caters to diverse learning styles and promotes deeper learning of what happens in workplaces.
    General Context of the Learning Situation

    What learning expectations are placed on this course or curriculum by: the school, district, university, college and/or department?  the profession?  society? 

    Learning expectations are designed by University Learning Outcomes for Quantitative Literacy and Accreditation Standards from the following bodies Joint Admissions and Matriculation Board, Joint Universities Preliminary Examination Board, Interim Joint Matriculation Board Examinations, National Universities Commission.

    Nature of the Subject

    Is this subject primarily theoretical, practical, or a combination?  Is the subject primarily convergent or divergent?  Are there important changes or controversies occurring within the field?

    This learning is a combination – This design adopts a shared control approach. Important changes are the rapid evolution in the ICT industry and changes to coding platforms. The Subject is primarily divergent and requires continuous learning. The instructor provides guidance and structure but empowers students to take ownership of their learning through:
    • In class lectures and recorded videos access on the LMS
    • self-paced online modules,
    • Google classroom for collaborations
    • group project work with presentations, and
    • Independent research.
    Characteristics of the Learners

    What is the life situation of the learners (e.g., socio-economic, cultural, personal, family, professional goals)?  What prior knowledge, experiences, and initial feelings do students usually have about this subject?  What are their learning goals and expectations?

    • Socio-economically, students who enroll for Advanced Level Training are in the upper 50 percentile economically, hence they can afford ICT gadgets, internet access and ensure power is available.
    • Students are graduates of high schools who are expected to have sound foundation al knowledge of statistics and programming.
    • Their learning goal is to leverage python programming to do the following:
      • Data analysis and data-based decision making
      • Data analytics for solving real world problems across various sectors.

       

      Characteristics of the Teacher

      What beliefs and values does the teacher have about teaching and learning?  What is his/her attitude toward:  the subject? students? What level of knowledge or familiarity does s/he has with this subject?  What are his/her strengths in teaching? 

      The following are the teacher’s learning philosophy and the plans adopted for training the students about the subject to ensure both competency-based and outcome-based learning are achieved for every student.

      Assessment for/as/of Learning:

      • Formative Assessments (For Learning): Online quizzes and discussion forums within the pre-recorded modules allow for self-assessment and peer feedback.
      • Summative Assessments (Of Learning): Midterm exams and final project assess overall understanding and application of data analysis concepts.
      • Performance- Based Assessments (As Learning): In-class group activities, group projects and presentations allow students to show their skills and receive immediate feedback.
      Moving Towards Deeper Learning:

      This design actively promotes deeper learning through:

      • Problem-solving activities: Students go beyond memorization by applying data analysis techniques to real-world business scenarios.
      • Collaborative projects: Working in teams encourages critical thinking, communication, and knowledge application.
      • Open-ended questions: Discussions and assignments encourage analysis, synthesis, and evaluation of data.

      The teachers are developers who use python programming for advanced data analytics, website development and product deployment. They have years of experience teaching and coaching students in data analytics across various fields such as healthcare, banking, aviation, agriculture, education etc.

       

      Characteristics of the Teacher

      What beliefs and values does the teacher have about teaching and learning?  What is his/her attitude toward:  the subject? students? What level of knowledge or familiarity does s/he has with this subject?  What are his/her strengths in teaching? 

      The following are the teacher’s learning philosophy and the plans adopted for training the students about the subject to ensure both competency-based and outcome-based learning are achieved for every student.

      Assessment for/as/of Learning:

      Moving Towards Deeper Learning:

      This design actively promotes deeper learning through:

      The teachers are developers who use python programming for advanced data analytics, website development and product deployment. They have years of experience teaching and coaching students in data analytics across various fields such as healthcare, banking, aviation, agriculture, education etc.

       

      Characteristics of the Teacher

      What beliefs and values does the teacher have about teaching and learning?  What is his/her attitude toward:  the subject? students? What level of knowledge or familiarity does s/he has with this subject?  What are his/her strengths in teaching? 

      The following are the teacher’s learning philosophy and the plans adopted for training the students about the subject to ensure both competency-based and outcome-based learning are achieved for every student.

      Assessment for/as/of Learning:

      Moving Towards Deeper Learning:

      This design actively promotes deeper learning through:

      The teachers are developers who use python programming for advanced data analytics, website development and product deployment. They have years of experience teaching and coaching students in data analytics across various fields such as healthcare, banking, aviation, agriculture, education etc.

      Learning Environment & Situational Factors to Consider

      How many students are in the class?  Is the course primary, secondary, undergraduate, or graduate level?  How long and frequent are the class meetings?  How will the course be delivered: live, online, blended, flipped or in a classroom or lab?  What physical elements of the learning environment will affect the class? What technology, networking and access issues will affect the class?

      1. Average of 35 students per class, Level of Instruction: Advanced Level Curriculum – Post Secondary or High School and Pre-University Educational system. This is equivalent to Year 1 College Education in the United States. Intended Audience: Advanced level Students.
      2. Class meetings are 2 hours each twice a week. 2 other online meetings via zoom. Interaction and collaboration on Google Meet. And tracking of work on Learndash which is the Learning management system.
      3. This approach combines online modules with interactive in-class sessions. Students learn foundational concepts through:
        • pre-recorded lectures and
        • activities outside of class in a real-world scenario like leveraging hospital data, etc.
        • Class time focuses on applying knowledge through discussions, problem-solving activities, and collaborative projects.
        • Exploring practice with python platforms – this caters to diverse learning styles and promotes deeper learning of what happens in workplaces.
      General Context of the Learning Situation

      What learning expectations are placed on this course or curriculum by: the school, district, university, college and/or department?  the profession?  society? 

      Learning expectations are designed by University Learning Outcomes for Quantitative Literacy and Accreditation Standards from the following bodies Joint Admissions and Matriculation Board, Joint Universities Preliminary Examination Board, Interim Joint Matriculation Board Examinations, National Universities Commission.

      Nature of the Subject

      Is this subject primarily theoretical, practical, or a combination?  Is the subject primarily convergent or divergent?  Are there important changes or controversies occurring within the field?

      This learning is a combination – This design adopts a shared control approach. Important changes are the rapid evolution in the ICT industry and changes to coding platforms. The Subject is primarily divergent and requires continuous learning. The instructor provides guidance and structure but empowers students to take ownership of their learning through:
      • In class lectures and recorded videos access on the LMS
      • self-paced online modules,
      • Google classroom for collaborations
      • group project work with presentations, and
      • Independent research.
      Characteristics of the Learners

      What is the life situation of the learners (e.g., socio-economic, cultural, personal, family, professional goals)?  What prior knowledge, experiences, and initial feelings do students usually have about this subject?  What are their learning goals and expectations?

      • Socio-economically, students who enroll for Advanced Level Training are in the upper 50 percentile economically, hence they can afford ICT gadgets, internet access and ensure power is available.
      • Students are graduates of high schools who are expected to have sound foundation al knowledge of statistics and programming.
      • Their learning goal is to leverage python programming to do the following:
        • Data analysis and data-based decision making
        • Data analytics for solving real world problems across various sectors.

       

      Characteristics of the Teacher

      What beliefs and values does the teacher have about teaching and learning?  What is his/her attitude toward:  the subject? students? What level of knowledge or familiarity does s/he has with this subject?  What are his/her strengths in teaching? 

      The following are the teacher’s learning philosophy and the plans adopted for training the students about the subject to ensure both competency-based and outcome-based learning are achieved for every student.

      Assessment for/as/of Learning:

      • Formative Assessments (For Learning): Online quizzes and discussion forums within the pre-recorded modules allow for self-assessment and peer feedback.
      • Summative Assessments (Of Learning): Midterm exams and final project assess overall understanding and application of data analysis concepts.
      • Performance- Based Assessments (As Learning): In-class group activities, group projects and presentations allow students to show their skills and receive immediate feedback.
      Moving Towards Deeper Learning:

      This design actively promotes deeper learning through:

      • Problem-solving activities: Students go beyond memorization by applying data analysis techniques to real-world business scenarios.
      • Collaborative projects: Working in teams encourages critical thinking, communication, and knowledge application.
      • Open-ended questions: Discussions and assignments encourage analysis, synthesis, and evaluation of data.

      The teachers are developers who use python programming for advanced data analytics, website development and product deployment. They have years of experience teaching and coaching students in data analytics across various fields such as healthcare, banking, aviation, agriculture, education etc.

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