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Using Machine Learning Techniques for Solving Locally Relevant Problems

The document discusses the work of the Datalab Research Group at the University of Zambia, which focuses on using machine learning for data-driven solutions to local problems. It outlines various projects including the online visibility of research, predicting student learning outcomes, and medical imaging workflows in Zambia. The document emphasizes the importance of data in creating effective machine learning models and highlights potential problems and future research areas.

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Deep Learning Indaba X - Zambia 2021 Lighton Phiri <lighton.phiri@unza.zm> Department of Library & Information Science University of Zambia http://lis.unza.zm/~lightonphiri Using Machine Learning Techniques for Solving Locally Relevant Problems
2 May 25, 2021 About The DataLab Research Group at The University of Zambia ● The DataLab research group at The University of Zambia is composed of faculty staff and students—undergraduate and postgraduate—working in three main areas ○ Data Mining ○ Digital Libraries ○ Technology-Enhanced Learning http://datalab.unza.zm
3 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems
4 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ○ Introduction ○ Data Mining Pipelines ○ Data Mining Models ● Part II. Past and Current Projects ● Part III. Potential Problems
5 May 25, 2021 Machine Learning 101 [...] https://commons.wikimedia.org/ ● Artificial Intelligence encompases a broad spectrum of sub-fields ○ Traditional machine learning techniques and approaches ○ Deep Learning approaches
6 May 25, 2021 Machine Learning 101 [...] https://commons.wikimedia.org/ ● Artificial Intelligence encompases a broad spectrum of sub-fields ○ Traditional machine learning techniques and approaches ○ Deep Learning approaches
7 May 25, 2021 Data is Key to ML-Centric Problem Solving
8 May 25, 2021 Data Mining Pipelines ● Fundamentally, machine learning aims to extract knowledge from data ○ Historical data is used to infer/predict outcomes associated with new observations
9 May 25, 2021 Data Mining Pipelines ● Input features identified during feature engineering are used to train models ○ Features correlated with outcome to be identified
10 May 25, 2021 Data Mining Pipelines ● The ML inference model is used to predict future patterns ○ Models can then be deployed as Web services and/or standalone applications
11 May 25, 2021 Data Mining Models (1/5) https://doi.org/10.1017/S0269888910000032 ● Numerous data mining models and frameworks have been proposed ○ Most trace their roots from the KDD Process proposed by Fayyad et al.
12 May 25, 2021 Data Mining Models (2/5) https://doi.org/10.1017/S0269888910000032
13 May 25, 2021 Data Mining Models (2/5) https://doi.org/10.1017/S0269888910000032
14 May 25, 2021 Data Mining Models (2/5) https://doi.org/10.1017/S0269888910000032
15 May 25, 2021 Data Mining Models (3/5) https://doi.org/10.1017/S0269888906000737
16 May 25, 2021 Data Mining Models (3/5) https://doi.org/10.1017/S0269888906000737
17 May 25, 2021 Data Mining Models (4/5) https://www.kdnuggets.com ● CRISP-DM model is one of the most widely used data mining models ● Data understanding and preparation are the most time consuming
18 May 25, 2021 Data Mining Models (5/5) https://arxiv.org/abs/2003.05155
19 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
20 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
21 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (1/4) https://worldmapper.org
22 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (1/4) https://worldmapper.org
23 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (2/4)
24 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (2/4)
25 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (3/4) http://www.webometrics.info
26 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (3/4) http://www.webometrics.info
27 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (4/4) Phiri, L. (2018) “Towards Increased Online Visibility of Scholarly Research Output in Zambia”. URL: http://lis.unza.zm/archive/handle/123456789/227
28 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (4/4) Phiri, L. (2018) “Towards Increased Online Visibility of Scholarly Research Output in Zambia”. URL: http://lis.unza.zm/archive/handle/123456789/227
29 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Problem (4/4) Phiri, L. (2018) “Towards Increased Online Visibility of Scholarly Research Output in Zambia”. URL: http://lis.unza.zm/archive/handle/123456789/227
30 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Multipronged Approach
31 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Multipronged Approach
32 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Multipronged Approach
33 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Multipronged Approach
34 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (1/7) ● Implementation of classification models to automatically classify IR digital objects using the minimum possible input from graduate students: “The ETD Manuscript” ○ The ETD manuscript bitstream is considered the “single source of truth” ○ Metadata prepared by staff that work with IR potentially have inconsistencies Phiri, L. (2021) “Automatic Classification of Digital Objects for Improved Metadata Quality of ETDs” URL: https://doi.org/10.1504/IJMSO.2020.112804
35 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (2/7) ● Text features extracted from a set of core bitstream portions—ETD Title, ETD Abstract, ETD Title Page and ETD pages—to classify ETD manuscripts ETD Type ETD Subjects IR Collection
36 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
37 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
38 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
39 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
40 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (4/7) ● OAI-PMH used to harvest all ETD descriptive metadata elements ● OAI-ORE used to harvest all ETD PDF documents
41 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (5/7) ● ETD Type—98.1% ● ETD Collection— 81.1% ● ETD Subjects—81.7% ● The models would still need to be incorporated into an application that requires “some” human intervention
42 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (6/7) https://github.com/lightonphiri/etd_autoclassifier
43 May 25, 2021 Project #1: Online Visibility of Research in Zambia—ETDs Automatic Classification (7/7) https://datalab-apis.herokuapp.com/api/collection
44 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Current Work (1/3) M’sendo R. (2019—Present) MSc Computer Science, University of Zambia “Multi-Faceted Automatic Classification of Institutional Repository Objects”
45 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Current Work (2/3) Chisale A. (2021—Present) MLIS, University of Zambia “Automatic Generation of Electronic Theses and Dissertations Metadata”
46 May 25, 2021 Project #1: Online Visibility of Research in Zambia—Current Work (3/3) http://lis.unza.zm/portal
47 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
48 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Problem (1/2) ● ICT 1110 performance is as issue. The poor performance transcends all assessments: quizzes, tests and practical programming questions.
49 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Problem (1/2) ● ICT 1110 performance is as issue. The poor performance transcends all assessments: quizzes, tests and practical programming questions.
50 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Problem (2/2) ● Potential solution: implement a prediction model aimed at identifying at-risk students . ○ Initiate interventions on at-risk students.
51 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Data Sources (1/5) ● Demographics information ● LMS interaction logs ● Course workload ● Subject responses
52 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Data Sources (2/5) ● Assessment results broken down by question ○ Concepts associated with question ○ Topics associated with question
53 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Data Sources (3/5) ● Assessment results broken down by question ○ Concepts associated with question ○ Topics associated with question
54 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Data Sources (4/5) ● LMS interaction logs ○ How often do students access Moodle (login attempts) ○ Which Moodle features are being access (GradeBook, Messaging) ○ Time spent on Moodle
55 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Data Sources (5/5) ● ICT 1110 information survey to capture information not available in SIS ○ Experience with computers ○ Motivation for taking the course ○ Specific location where student lives (although this can be inferred from next of kin address perhaps?)
56 May 25, 2021 Project #2: Predicting Student Learning Outcomes—Current Work Chaibela, M., Chisha, I., Pungwa, D., Siabbaba D. and Simukoko B. (2021) “Performance Predictor: Machine Learning Tool for Student Performance Outcomes”. Work-in-Progress
57 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
58 May 25, 2021 Project #3: Medical Imaging Workflows in Zambia—Problem https://mjz.co.zm/index.php/mjz/article/view/560
59 May 25, 2021 Project #3: Medical Imaging Workflows in Zambia—Current Work (1/2)
60 May 25, 2021 Project #3: Medical Imaging Workflows in Zambia—Current Work (1/2)
61 May 25, 2021 Project #3: Medical Imaging Workflows in Zambia—Current Work (2/2)
62 May 25, 2021 Project #3: Medical Imaging Workflows in Zambia—Current Work (2/2)
63 May 25, 2021 Project #3: Medical Imaging Workflows in Zambia—Current Work (2/2)
64 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
65 May 25, 2021 Project #3: Automatic Forecasting of Seasonal Rainfall—Current Work
66 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems ○ Exemplar Projects in Zambia ○ Potential Locally Relevant Problems
67 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems ○ Exemplar Projects in Zambia ○ Potential Locally Relevant Problems
68 May 25, 2021 Agriculture: Automatic identification and Early Warning of Fall Armyworms http://dspace.unza.zm/handle/123456789/7141
69 May 25, 2021 Telecommunications: Automatic Customer Segmentation http://dspace.unza.zm/handle/123456789/7069
70 May 25, 2021 Banking: Automatic Data Mining for Fraud Detection https://bit.ly/3wxJICk
71 May 25, 2021 Outline ● Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems ○ Exemplar Projects in Zambia ○ Potential Locally Relevant Problems
72 May 25, 2021 Potential Locally Relevant Problems in Zambia (1/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
73 May 25, 2021 Potential Locally Relevant Problems in Zambia (2/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps? Zambia Daily Mail | August 18, 2019 | Volume 22 No. 033
74 May 25, 2021 Potential Locally Relevant Problems in Zambia (3/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
75 May 25, 2021 Potential Locally Relevant Problems in Zambia (3/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
76 May 25, 2021 Potential Locally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
77 May 25, 2021 Potential Locally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
78 May 25, 2021 Potential Locally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
79 May 25, 2021 Potential Locally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
80 May 25, 2021 Potential Locally Relevant Problems in Zambia (5/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
81 May 25, 2021 Potential Locally Relevant Problems in Zambia (5/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
82 May 25, 2021 Potential Locally Relevant Problems in Zambia (6/6) ● Education ● Health ● So-called ICT for development perhaps?
83 May 25, 2021 Potential Locally Relevant Problems in Zambia (6/6) ● Education ● Health ● So-called ICT for development perhaps?
84 May 25, 2021 Q & A Session ● Comments, concerns and complaints?
[1] Phiri, L. (2018). Research Visibility in the Global South: Towards Increased Online Visibility of Scholarly Research Output in Zambia. IEEE International Conference in Information and Communication Technologies. [2] Phiri, L. (2020). A Multi-Faceted Multi-Stakeholder Approach for Increased Visibility of ETDs in Zambia. Cadernos BAD, (1). https://doi.org/10.1017/S0269888910000032 [3] Phiri, L. (2020). Automatic classification of digital objects for improved metadata quality of electronic theses and dissertations in institutional repositories. International Journal of Metadata, Semantics and Ontologies, 14(3), 234-248. Bibliography
lighton.phiri@unza.zm http://datalab.unza.zm http://lis.unza.zm/~lightonphiri

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Using Machine Learning Techniques for Solving Locally Relevant Problems

  • 1.
    Deep Learning Indaba X- Zambia 2021 Lighton Phiri <[email protected]> Department of Library & Information Science University of Zambia http://lis.unza.zm/~lightonphiri Using Machine Learning Techniques for Solving Locally Relevant Problems
  • 2.
    2 May 25, 2021 AboutThe DataLab Research Group at The University of Zambia ● The DataLab research group at The University of Zambia is composed of faculty staff and students—undergraduate and postgraduate—working in three main areas ○ Data Mining ○ Digital Libraries ○ Technology-Enhanced Learning http://datalab.unza.zm
  • 3.
    3 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems
  • 4.
    4 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ○ Introduction ○ Data Mining Pipelines ○ Data Mining Models ● Part II. Past and Current Projects ● Part III. Potential Problems
  • 5.
    5 May 25, 2021 MachineLearning 101 [...] https://commons.wikimedia.org/ ● Artificial Intelligence encompases a broad spectrum of sub-fields ○ Traditional machine learning techniques and approaches ○ Deep Learning approaches
  • 6.
    6 May 25, 2021 MachineLearning 101 [...] https://commons.wikimedia.org/ ● Artificial Intelligence encompases a broad spectrum of sub-fields ○ Traditional machine learning techniques and approaches ○ Deep Learning approaches
  • 7.
    7 May 25, 2021 Datais Key to ML-Centric Problem Solving
  • 8.
    8 May 25, 2021 DataMining Pipelines ● Fundamentally, machine learning aims to extract knowledge from data ○ Historical data is used to infer/predict outcomes associated with new observations
  • 9.
    9 May 25, 2021 DataMining Pipelines ● Input features identified during feature engineering are used to train models ○ Features correlated with outcome to be identified
  • 10.
    10 May 25, 2021 DataMining Pipelines ● The ML inference model is used to predict future patterns ○ Models can then be deployed as Web services and/or standalone applications
  • 11.
    11 May 25, 2021 DataMining Models (1/5) https://doi.org/10.1017/S0269888910000032 ● Numerous data mining models and frameworks have been proposed ○ Most trace their roots from the KDD Process proposed by Fayyad et al.
  • 12.
    12 May 25, 2021 DataMining Models (2/5) https://doi.org/10.1017/S0269888910000032
  • 13.
    13 May 25, 2021 DataMining Models (2/5) https://doi.org/10.1017/S0269888910000032
  • 14.
    14 May 25, 2021 DataMining Models (2/5) https://doi.org/10.1017/S0269888910000032
  • 15.
    15 May 25, 2021 DataMining Models (3/5) https://doi.org/10.1017/S0269888906000737
  • 16.
    16 May 25, 2021 DataMining Models (3/5) https://doi.org/10.1017/S0269888906000737
  • 17.
    17 May 25, 2021 DataMining Models (4/5) https://www.kdnuggets.com ● CRISP-DM model is one of the most widely used data mining models ● Data understanding and preparation are the most time consuming
  • 18.
    18 May 25, 2021 DataMining Models (5/5) https://arxiv.org/abs/2003.05155
  • 19.
    19 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
  • 20.
    20 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
  • 21.
    21 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (1/4) https://worldmapper.org
  • 22.
    22 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (1/4) https://worldmapper.org
  • 23.
    23 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (2/4)
  • 24.
    24 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (2/4)
  • 25.
    25 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (3/4) http://www.webometrics.info
  • 26.
    26 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (3/4) http://www.webometrics.info
  • 27.
    27 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (4/4) Phiri, L. (2018) “Towards Increased Online Visibility of Scholarly Research Output in Zambia”. URL: http://lis.unza.zm/archive/handle/123456789/227
  • 28.
    28 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (4/4) Phiri, L. (2018) “Towards Increased Online Visibility of Scholarly Research Output in Zambia”. URL: http://lis.unza.zm/archive/handle/123456789/227
  • 29.
    29 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Problem (4/4) Phiri, L. (2018) “Towards Increased Online Visibility of Scholarly Research Output in Zambia”. URL: http://lis.unza.zm/archive/handle/123456789/227
  • 30.
    30 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Multipronged Approach
  • 31.
    31 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Multipronged Approach
  • 32.
    32 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Multipronged Approach
  • 33.
    33 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Multipronged Approach
  • 34.
    34 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (1/7) ● Implementation of classification models to automatically classify IR digital objects using the minimum possible input from graduate students: “The ETD Manuscript” ○ The ETD manuscript bitstream is considered the “single source of truth” ○ Metadata prepared by staff that work with IR potentially have inconsistencies Phiri, L. (2021) “Automatic Classification of Digital Objects for Improved Metadata Quality of ETDs” URL: https://doi.org/10.1504/IJMSO.2020.112804
  • 35.
    35 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (2/7) ● Text features extracted from a set of core bitstream portions—ETD Title, ETD Abstract, ETD Title Page and ETD pages—to classify ETD manuscripts ETD Type ETD Subjects IR Collection
  • 36.
    36 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
  • 37.
    37 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
  • 38.
    38 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
  • 39.
    39 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (3/7) ● Textual content mined from PDF manuscripts ○ Cover/title pages ○ Preliminary pages ● Textual content mined from metadata for training ● PDF document metadata ● Curated datasets from external repositories
  • 40.
    40 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (4/7) ● OAI-PMH used to harvest all ETD descriptive metadata elements ● OAI-ORE used to harvest all ETD PDF documents
  • 41.
    41 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (5/7) ● ETD Type—98.1% ● ETD Collection— 81.1% ● ETD Subjects—81.7% ● The models would still need to be incorporated into an application that requires “some” human intervention
  • 42.
    42 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (6/7) https://github.com/lightonphiri/etd_autoclassifier
  • 43.
    43 May 25, 2021 Project#1: Online Visibility of Research in Zambia—ETDs Automatic Classification (7/7) https://datalab-apis.herokuapp.com/api/collection
  • 44.
    44 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Current Work (1/3) M’sendo R. (2019—Present) MSc Computer Science, University of Zambia “Multi-Faceted Automatic Classification of Institutional Repository Objects”
  • 45.
    45 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Current Work (2/3) Chisale A. (2021—Present) MLIS, University of Zambia “Automatic Generation of Electronic Theses and Dissertations Metadata”
  • 46.
    46 May 25, 2021 Project#1: Online Visibility of Research in Zambia—Current Work (3/3) http://lis.unza.zm/portal
  • 47.
    47 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
  • 48.
    48 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Problem (1/2) ● ICT 1110 performance is as issue. The poor performance transcends all assessments: quizzes, tests and practical programming questions.
  • 49.
    49 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Problem (1/2) ● ICT 1110 performance is as issue. The poor performance transcends all assessments: quizzes, tests and practical programming questions.
  • 50.
    50 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Problem (2/2) ● Potential solution: implement a prediction model aimed at identifying at-risk students . ○ Initiate interventions on at-risk students.
  • 51.
    51 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Data Sources (1/5) ● Demographics information ● LMS interaction logs ● Course workload ● Subject responses
  • 52.
    52 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Data Sources (2/5) ● Assessment results broken down by question ○ Concepts associated with question ○ Topics associated with question
  • 53.
    53 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Data Sources (3/5) ● Assessment results broken down by question ○ Concepts associated with question ○ Topics associated with question
  • 54.
    54 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Data Sources (4/5) ● LMS interaction logs ○ How often do students access Moodle (login attempts) ○ Which Moodle features are being access (GradeBook, Messaging) ○ Time spent on Moodle
  • 55.
    55 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Data Sources (5/5) ● ICT 1110 information survey to capture information not available in SIS ○ Experience with computers ○ Motivation for taking the course ○ Specific location where student lives (although this can be inferred from next of kin address perhaps?)
  • 56.
    56 May 25, 2021 Project#2: Predicting Student Learning Outcomes—Current Work Chaibela, M., Chisha, I., Pungwa, D., Siabbaba D. and Simukoko B. (2021) “Performance Predictor: Machine Learning Tool for Student Performance Outcomes”. Work-in-Progress
  • 57.
    57 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
  • 58.
    58 May 25, 2021 Project#3: Medical Imaging Workflows in Zambia—Problem https://mjz.co.zm/index.php/mjz/article/view/560
  • 59.
    59 May 25, 2021 Project#3: Medical Imaging Workflows in Zambia—Current Work (1/2)
  • 60.
    60 May 25, 2021 Project#3: Medical Imaging Workflows in Zambia—Current Work (1/2)
  • 61.
    61 May 25, 2021 Project#3: Medical Imaging Workflows in Zambia—Current Work (2/2)
  • 62.
    62 May 25, 2021 Project#3: Medical Imaging Workflows in Zambia—Current Work (2/2)
  • 63.
    63 May 25, 2021 Project#3: Medical Imaging Workflows in Zambia—Current Work (2/2)
  • 64.
    64 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ○ Scholarly Research Output in Zambia ○ Predicting Learning Outcome at UNZA ○ Medical Imaging Workflows in Zambia ○ Automatic Weather Prediction in Zambia ● Part III. Potential Problems
  • 65.
    65 May 25, 2021 Project#3: Automatic Forecasting of Seasonal Rainfall—Current Work
  • 66.
    66 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems ○ Exemplar Projects in Zambia ○ Potential Locally Relevant Problems
  • 67.
    67 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems ○ Exemplar Projects in Zambia ○ Potential Locally Relevant Problems
  • 68.
    68 May 25, 2021 Agriculture:Automatic identification and Early Warning of Fall Armyworms http://dspace.unza.zm/handle/123456789/7141
  • 69.
    69 May 25, 2021 Telecommunications:Automatic Customer Segmentation http://dspace.unza.zm/handle/123456789/7069
  • 70.
    70 May 25, 2021 Banking:Automatic Data Mining for Fraud Detection https://bit.ly/3wxJICk
  • 71.
    71 May 25, 2021 Outline ●Part I. Data-Driven Problem Solving ● Part II. Past and Current Projects ● Part III. Potential Problems ○ Exemplar Projects in Zambia ○ Potential Locally Relevant Problems
  • 72.
    72 May 25, 2021 PotentialLocally Relevant Problems in Zambia (1/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 73.
    73 May 25, 2021 PotentialLocally Relevant Problems in Zambia (2/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps? Zambia Daily Mail | August 18, 2019 | Volume 22 No. 033
  • 74.
    74 May 25, 2021 PotentialLocally Relevant Problems in Zambia (3/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 75.
    75 May 25, 2021 PotentialLocally Relevant Problems in Zambia (3/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 76.
    76 May 25, 2021 PotentialLocally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 77.
    77 May 25, 2021 PotentialLocally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 78.
    78 May 25, 2021 PotentialLocally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 79.
    79 May 25, 2021 PotentialLocally Relevant Problems in Zambia (4/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 80.
    80 May 25, 2021 PotentialLocally Relevant Problems in Zambia (5/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 81.
    81 May 25, 2021 PotentialLocally Relevant Problems in Zambia (5/6) ● Impact-driven research/studies ○ Education ○ Health ○ So-called ICT for development perhaps?
  • 82.
    82 May 25, 2021 PotentialLocally Relevant Problems in Zambia (6/6) ● Education ● Health ● So-called ICT for development perhaps?
  • 83.
    83 May 25, 2021 PotentialLocally Relevant Problems in Zambia (6/6) ● Education ● Health ● So-called ICT for development perhaps?
  • 84.
    84 May 25, 2021 Q& A Session ● Comments, concerns and complaints?
  • 85.
    [1] Phiri, L.(2018). Research Visibility in the Global South: Towards Increased Online Visibility of Scholarly Research Output in Zambia. IEEE International Conference in Information and Communication Technologies. [2] Phiri, L. (2020). A Multi-Faceted Multi-Stakeholder Approach for Increased Visibility of ETDs in Zambia. Cadernos BAD, (1). https://doi.org/10.1017/S0269888910000032 [3] Phiri, L. (2020). Automatic classification of digital objects for improved metadata quality of electronic theses and dissertations in institutional repositories. International Journal of Metadata, Semantics and Ontologies, 14(3), 234-248. Bibliography
  • 86.