2022/3, Trimester 1, FACE-TO-FACE,
VIEW FULL DETAILS
Occurrence: | 001 |
Primary mode of delivery: | FACE-TO-FACE |
Location of delivery: | SIGHTHILL |
Partner: | |
Member of staff responsible for delivering module: | Roberto Kulpa |
Module Organiser: | |
Learning, Teaching and Assessment (LTA) Approach: |
Teaching and leaning on the Understanding and Executing Quantitative Research module develops across several modalities and channels of delivery:• through the regular weekly workshops, scheduled guest sessions, periodic programme-exclusive events, • delivered on campus, online, through fieldwork and external visits,• enhanced through individual, personalised tutoring and mentorship activities,• regularly revised via formative feedback, self-evaluation exercises, and diverse assessment strategies enhancing and mobilising further learning.We will make use of weekly 3h timeslots to deliver workshops that are creatively tailored to the applied and participatory nature of this module, responding to the group needs and (possibly diverse) level of research experience among students. Each workshop will have tutor’s and students’ input delivered in various formats, for instance: pre-recorded lecture or a mix of pre-recorded and in-class shorter presentations; student presentations; pair and small group discussions, all-class forum debates; problem-solving practical tasks and activities; computer lab work; 1-on-1 student-tutor time, peer-learning exercises. We will also use blended learning technologies in response to your needs, and diverse and applied nature of learned and practised content. We envisage making use of pre-recorded lectures; live, online meetings using WebEx, interactive VLE (Virtual Learning Environment) technologies such as Moodle quizzes, peer review and feedback workshops, online annotation and commentary tools on e-book platforms (Kortext or similar). We will also use computer labs to work with professional surveying/statistical software.In particular, on this module:• Lectures and in-class discussions will help you advance the understanding of the complex links between theory and methodology of quantitative research (LO1), and with critically reflecting on the uses and applications of data science (LO4).• Practical tasks and small group exercises, incl. guided activities in the computer lab, will stimulate your imagination and creativity to demonstrate your ability to construct quantitative research tools and perform operations with the relevant software (LO2,3).• Guided self-study and individual scholarship time that you dedicate to learning on this module, will form a core of your learning, supporting your achievement of module’s aims and objectives (LO1,2,3,4).
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Core formative feedback will happen in-class during the workshops. In each workshop session we practice critical capacities and skills of the applied work with/on quantitative data. You will receive feedback on your individual and small group work, ensuring tailored commentary to support your growth. You will also learn from peer-feedback, when you will be asked to review and check other students work and explain your rationale. This peer-review exercise is also a formative preparation for the Evaluation and Impact module, where you will learn professional evaluation strategies, consistently building cross-module feedback structure on our programme.
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You will submit Portfolio (100% weight of the final mark), in which you will include at minimum two outputs:• 50% - Research Tool (corresponding to assessed LOs: 2,3).• 50% - Critical Annotation and Reflection on the research tool (corresponding to assessed LOs: 1,4) (indicative word length: 1,000).Length and format will be specified by and agreed with the ML, corresponding to format and specific choices & requirements of the chosen research tool and researched topic. Please note: it is not possible to specify the exact (or even indicative) word length, as the assessment asks for the research tool that is not word counted.You will collect a portfolio evidencing designed, created and executed a research tool (LO2) using suitable techniques, tools, pre-existing/dummy datasets and software. You will present the output generated by the tool in a suitable format, evidencing your ability to perform quantitative research (LO3). Having executed the applied part, you will write a critical annotation on the tool, incl. self-reflection on the process, evidencing your critical understanding of quantitative research principles (LO1,4).
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Student Activity (Notional Equivalent Study Hours (NESH)) |
Mode of activity | Learning & Teaching Activity | NESH (Study Hours) |
Face To Face | Practical classes and workshops | 36 |
Independent Learning | Groupwork (Independent Study) | 24 |
Independent Learning | Guided independent study | 140 |
| Total Study Hours | 200 |
| Expected Total Study Hours for Module | 200 |
Assessment |
Type of Assessment | Weighting % | LOs covered | Week due | Length in Hours/Words |
Portfolio | 100 | 1,2,3,4 | 12 | , WORDS= 1000 |
Component 1 subtotal: | 100 | |
Component 2 subtotal: | 0 | | | |
Module subtotal: | 100 | | | |
2022/3, Trimester 1, FACE-TO-FACE,
VIEW FULL DETAILS
Occurrence: | 002 |
Primary mode of delivery: | FACE-TO-FACE |
Location of delivery: | SIGHTHILL |
Partner: | |
Member of staff responsible for delivering module: | Roberto Kulpa |
Module Organiser: | |
Learning, Teaching and Assessment (LTA) Approach: |
Teaching and leaning on the Understanding and Executing Quantitative Research module develops across several modalities and channels of delivery:• through the regular weekly workshops, scheduled guest sessions, periodic programme-exclusive events, • delivered on campus, online, through fieldwork and external visits,• enhanced through individual, personalised tutoring and mentorship activities,• regularly revised via formative feedback, self-evaluation exercises, and diverse assessment strategies enhancing and mobilising further learning.We will make use of weekly 3h timeslots to deliver workshops that are creatively tailored to the applied and participatory nature of this module, responding to the group needs and (possibly diverse) level of research experience among students. Each workshop will have tutor’s and students’ input delivered in various formats, for instance: pre-recorded lecture or a mix of pre-recorded and in-class input; student presentations; pair and small group discussions, all-class forum debates; problem-solving practical tasks and activities; computer lab work; 1-on-1 student-tutor time, peer-learning exercises. We will also use various learning technologies, on top of regular in-person teaching, in response to your needs, and diverse and applied nature of learned and practised content. We envisage making use of pre-recorded lectures; live, online meetings using WebEx, interactive VLE (Virtual Learning Environment) technologies such as Moodle quizzes, peer review and feedback workshops, online annotation and commentary tools on e-book platforms (Kortext or similar). We will also use computer labs to work with professional surveying/statistical software.In particular, on this module:• Lectures and in-class discussions will help you advance the understanding of the complex links between theory and methodology of quantitative research (LO1), and with critically reflecting on the uses and applications of data science (LO4).• Practical tasks and small group exercises, incl. guided activities in the computer lab, will stimulate your imagination and creativity to demonstrate your ability to construct quantitative research tools and perform operations with the relevant software (LO2,3).• Guided self-study and individual scholarship time that you dedicate to learning on this module, will form a core of your learning, supporting your achievement of module’s aims and objectives (LO1,2,3,4).
|
Core formative feedback will happen in-class during the workshops. In each workshop session we practice critical capacities and skills of the applied work with/on quantitative data. You will receive feedback on your individual and small group work, ensuring tailored commentary to support your growth. You will also learn from peer-feedback, when you will be asked to review and check other students work and explain your rationale. This peer-review exercise is also a formative preparation for the Evaluation and Impact module, where you will learn professional evaluation strategies, consistently building cross-module feedback structure on our programme.
|
You will submit Portfolio (100% weight of the final mark), in which you will include at minimum two outputs:• 50% - Research Tool (corresponding to assessed LOs: 2,3).• 50% - Critical Annotation and Reflection on the research tool (corresponding to assessed LOs: 1,4) (indicative word length: 1000).Length and format will be specified by and agreed with the ML, corresponding to the format and specific choices & requirements of the chosen research tool. Please note: it is not possible to specify the exact (or even indicative) word length, as the assessment asks for the research tool that is not word counted.You will collect a portfolio evidencing designed, created and executed a research tool (LO2) using suitable techniques, tools, pre-existing/dummy datasets and software. You will present the output generated by the tool in a suitable format, evidencing your ability to perform quantitative research (LO3). Having executed the applied part, you will write a critical annotation on the tool, incl. self-reflection on the process, evidencing your critical understanding of quantitative research principles (LO1,4).
|
Student Activity (Notional Equivalent Study Hours (NESH)) |
Mode of activity | Learning & Teaching Activity | NESH (Study Hours) |
Face To Face | Practical classes and workshops | 36 |
Independent Learning | Groupwork (Independent Study) | 24 |
Independent Learning | Guided independent study | 140 |
| Total Study Hours | 200 |
| Expected Total Study Hours for Module | 200 |
Assessment |
Type of Assessment | Weighting % | LOs covered | Week due | Length in Hours/Words |
Portfolio | 100 | 1,2,3,4 | 12 | |
Component 1 subtotal: | 100 | |
Component 2 subtotal: | 0 | | | |
Module subtotal: | 100 | | | |