(2025-09-30) Target: Crawler can run successfully

1. S3 bucket
   create s3:
   name: c3l-engage-ai-raw-dataset-t
2. Data
3. Database
   create glue database:

aws glue create-database \
  --database-input '{"Name":"my_glue_db","Description":"Glue database for S3 tables"}' \
  --profile dev-admin

Script for calculating claim hours

import pandas as pd
import re

claim_info = """
16 May 2024	Project Name	3 hrs		Approved
17 May 2024	Project Name	2 hrs		Approved
20 May 2024	Project Name	2 hrs 30 mins		Approved
21 May 2024	Project Name	4 hrs 30 mins		Approved
22 May 2024	Project Name	1 hr 30 mins		Approved
27 May 2024	Project Name	2 hrs 30 mins		Approved
28 May 2024	Project Name	4 hrs 30 mins		Approved
31 May 2024	Project Name	2 hrs 30 mins		Approved
04 Jun 2024	Project Name	4 hrs		Submitted
05 Jun 2024	Project Name	4 hrs		Submitted
07 Jun 2024	Project Name	2 hrs		Submitted
04 Jun 2024	Project Name	4 hrs		Approved
05 Jun 2024	Project Name	4 hrs		Approved
"""

df_all_info = pd.DataFrame(
    [list(filter(lambda x: len(x) > 0, i.split("\t"))) for i in filter(lambda x: len(x) > 0, claim_info.split("\n"))]
)
df_all_info.columns = ["Date", "Role", "Hours", "Status"]

# Regular expression to capture hours and minutes
pattern = r'(\d+)\s+hrs(?:\s+(\d+)\s+mins)?'
total= 0
for i in df_all_info[df_all_info["Status"] == "Approved"]["Hours"]:
    matches = re.findall(pattern, i)
    if len(matches) > 0:
        hour, minute = matches[0]
        total += int(f"0{hour}") + int(f"0{minute}")/60
print("Total Claim Hours:", total)

Question**: Interventions that inflence Access Frequency

Key

• Course Requirements and Assignments: The nature and frequency of assignments, quizzes, and exams can drive students to access course materials more frequently. Deadlines and project milestones also play a role.
• Engaging Content: Courses that include interactive elements such as videos, discussions, and hands-on activities often see higher engagement. Gamification elements, like quizzes and leaderboards, can also increase access frequency.
• Instructor Engagement: Active participation and feedback from instructors can encourage students to visit course platforms more often. Regular announcements, office hours, and Q&A sessions can keep students engaged.
• Peer Interaction: Discussion forums, group projects, and peer feedback opportunities can motivate students to access educational resources more frequently to engage with their classmates.
• Learning Tools and Resources: Availability of supplementary materials like readings, videos, tutorials, and external resources can encourage students to explore and revisit the content.
• Accessibility and Convenience: The ease of access to the platform or resources, including mobile-friendly interfaces, offline access, and intuitive navigation, can significantly impact how often students engage with the material.
• Personal Motivation and Goals: Students' individual motivation, such as interest in the subject, career aspirations, and personal goals, can drive more frequent engagement with educational resources.
• Feedback and Assessment: Regular feedback, assessments, and the opportunity to track progress can motivate students to engage more frequently.

This project is for the setup/execution of the Human Systems Dialogic Agent.

The solution uses AWS CDK to automate the compilation of CloudFormation code for deployment to Human Systems' Dialogic Agent AWS Environments (i.e. 'HS-DIALOGIC-AGENT-DEV', 'HS-DIALOGIC-AGENT-STAGE' and 'HS-DIALOGIC-AGENT-PROD' accounts).

Steps

Step 1: run docker container

docker run -P -itd -v D:\workspace\10.Unisa\HS-System:/home/hs --name da ubuntu

Step 2: install python

## Update repository
apt update
apt-get install software-properties-common
add-apt-repository ppa:deadsnakes/ppa 

## install python
apt install python3.11
ln -s /usr/bin/python3.11 /usr/bin/python3
ln -s /usr/bin/python3 /usr/bin/python
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py
pip install poetry



## install node
#https://nodejs.org/en/download/prebuilt-binaries

Description

I need your help with one of the student research projects, while we're waiting for further input on the Human Systems work.

What I need to do is to send a message to openAI API. OpenAI API should run the same prompt 5 times (like bootstrapping) and return a file with results for each run

::: echarts Curriculums

myChart.showLoading();

fetch('data/curriculums.json')
  .then(response => response.json())
  .then(data => {
    myChart.hideLoading();
    myChart.setOption(
      (option = {
        tooltip: {
          trigger: 'item',
          triggerOn: 'mousemove',
          formatter: function (params) {
            return `
              <div style="text-align: left;">
                <strong>Curriculum: </strong>${params.data.unique_key}<br/>
                <strong>Title</strong>: ${params.data.title}<br/>
                <strong>Description</strong>: ${params.data.description}<br/>
              </div>
            `;
          }
        },
        legend: {
          top: '2%',
          left: '3%',
          orient: 'vertical',
          data: [
            {
              name: 'Curriculum (Old)',
              icon: 'rectangle'
            }
          ],
          borderColor: '#c23531'
        },
        series: [
          {initialTreeDepth: -1,
            type: 'tree',
            name: 'tree1',
            data: [data],
            top: '5%',
            left: '17%',
            bottom: '2%',
            right: '20%',
            symbolSize: 7,
            label: {
              position: 'left',
              verticalAlign: 'middle',
              align: 'right'
            },
            leaves: {
              label: {
                position: 'right',
                verticalAlign: 'middle',
                align: 'left'
              }
            },
            emphasis: {
              focus: 'descendant'
            },
            expandAndCollapse: true,
            animationDuration: 550,
            animationDurationUpdate: 750
          }
        ]
      })
    );
    chart.setOption(option);
  })
  .catch(error => {
    console.error('Error loading JSON data:', error);
  });