Module 3: Learning Analytics

.title[
# Module 3: Learning Analytics
]
.subtitle[
## EME6356: Learning & Web Analytics
]
.author[
### Dr. Bret Staudt Willet
]
.date[
### June 5, 2023
]

---

# <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M160 80H512c8.8 0 16 7.2 16 16V320c0 8.8-7.2 16-16 16H490.8L388.1 178.9c-4.4-6.8-12-10.9-20.1-10.9s-15.7 4.1-20.1 10.9l-52.2 79.8-12.4-16.9c-4.5-6.2-11.7-9.8-19.4-9.8s-14.8 3.6-19.4 9.8L175.6 336H160c-8.8 0-16-7.2-16-16V96c0-8.8 7.2-16 16-16zM96 96V320c0 35.3 28.7 64 64 64H512c35.3 0 64-28.7 64-64V96c0-35.3-28.7-64-64-64H160c-35.3 0-64 28.7-64 64zM48 120c0-13.3-10.7-24-24-24S0 106.7 0 120V344c0 75.1 60.9 136 136 136H456c13.3 0 24-10.7 24-24s-10.7-24-24-24H136c-48.6 0-88-39.4-88-88V120zm208 24a32 32 0 1 0 -64 0 32 32 0 1 0 64 0z"/></svg>

**View the slides:**

[bretsw.com/eme6356-su23-module3](https://bretsw.com/eme6356-su23-module3)

---

---

- How well can data capture our multifaceted selves?

- Do data shape our identities?

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M323.8 34.8c-38.2-10.9-78.1 11.2-89 49.4l-5.7 20c-3.7 13-10.4 25-19.5 35l-51.3 56.4c-8.9 9.8-8.2 25 1.6 33.9s25 8.2 33.9-1.6l51.3-56.4c14.1-15.5 24.4-34 30.1-54.1l5.7-20c3.6-12.7 16.9-20.1 29.7-16.5s20.1 16.9 16.5 29.7l-5.7 20c-5.7 19.9-14.7 38.7-26.6 55.5c-5.2 7.3-5.8 16.9-1.7 24.9s12.3 13 21.3 13L448 224c8.8 0 16 7.2 16 16c0 6.8-4.3 12.7-10.4 15c-7.4 2.8-13 9-14.9 16.7s.1 15.8 5.3 21.7c2.5 2.8 4 6.5 4 10.6c0 7.8-5.6 14.3-13 15.7c-8.2 1.6-15.1 7.3-18 15.1s-1.6 16.7 3.6 23.3c2.1 2.7 3.4 6.1 3.4 9.9c0 6.7-4.2 12.6-10.2 14.9c-11.5 4.5-17.7 16.9-14.4 28.8c.4 1.3 .6 2.8 .6 4.3c0 8.8-7.2 16-16 16H286.5c-12.6 0-25-3.7-35.5-10.7l-61.7-41.1c-11-7.4-25.9-4.4-33.3 6.7s-4.4 25.9 6.7 33.3l61.7 41.1c18.4 12.3 40 18.8 62.1 18.8H384c34.7 0 62.9-27.6 64-62c14.6-11.7 24-29.7 24-50c0-4.5-.5-8.8-1.3-13c15.4-11.7 25.3-30.2 25.3-51c0-6.5-1-12.8-2.8-18.7C504.8 273.7 512 257.7 512 240c0-35.3-28.6-64-64-64l-92.3 0c4.7-10.4 8.7-21.2 11.8-32.2l5.7-20c10.9-38.2-11.2-78.1-49.4-89zM32 192c-17.7 0-32 14.3-32 32V448c0 17.7 14.3 32 32 32H96c17.7 0 32-14.3 32-32V224c0-17.7-14.3-32-32-32H32z"/></svg> <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M323.8 477.2c-38.2 10.9-78.1-11.2-89-49.4l-5.7-20c-3.7-13-10.4-25-19.5-35l-51.3-56.4c-8.9-9.8-8.2-25 1.6-33.9s25-8.2 33.9 1.6l51.3 56.4c14.1 15.5 24.4 34 30.1 54.1l5.7 20c3.6 12.7 16.9 20.1 29.7 16.5s20.1-16.9 16.5-29.7l-5.7-20c-5.7-19.9-14.7-38.7-26.6-55.5c-5.2-7.3-5.8-16.9-1.7-24.9s12.3-13 21.3-13L448 288c8.8 0 16-7.2 16-16c0-6.8-4.3-12.7-10.4-15c-7.4-2.8-13-9-14.9-16.7s.1-15.8 5.3-21.7c2.5-2.8 4-6.5 4-10.6c0-7.8-5.6-14.3-13-15.7c-8.2-1.6-15.1-7.3-18-15.2s-1.6-16.7 3.6-23.3c2.1-2.7 3.4-6.1 3.4-9.9c0-6.7-4.2-12.6-10.2-14.9c-11.5-4.5-17.7-16.9-14.4-28.8c.4-1.3 .6-2.8 .6-4.3c0-8.8-7.2-16-16-16H286.5c-12.6 0-25 3.7-35.5 10.7l-61.7 41.1c-11 7.4-25.9 4.4-33.3-6.7s-4.4-25.9 6.7-33.3l61.7-41.1c18.4-12.3 40-18.8 62.1-18.8H384c34.7 0 62.9 27.6 64 62c14.6 11.7 24 29.7 24 50c0 4.5-.5 8.8-1.3 13c15.4 11.7 25.3 30.2 25.3 51c0 6.5-1 12.8-2.8 18.7C504.8 238.3 512 254.3 512 272c0 35.3-28.6 64-64 64l-92.3 0c4.7 10.4 8.7 21.2 11.8 32.2l5.7 20c10.9 38.2-11.2 78.1-49.4 89zM32 384c-17.7 0-32-14.3-32-32V128c0-17.7 14.3-32 32-32H96c17.7 0 32 14.3 32 32V352c0 17.7-14.3 32-32 32H32z"/></svg> Double-Edged Sword

- How can data **help**? <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M323.8 34.8c-38.2-10.9-78.1 11.2-89 49.4l-5.7 20c-3.7 13-10.4 25-19.5 35l-51.3 56.4c-8.9 9.8-8.2 25 1.6 33.9s25 8.2 33.9-1.6l51.3-56.4c14.1-15.5 24.4-34 30.1-54.1l5.7-20c3.6-12.7 16.9-20.1 29.7-16.5s20.1 16.9 16.5 29.7l-5.7 20c-5.7 19.9-14.7 38.7-26.6 55.5c-5.2 7.3-5.8 16.9-1.7 24.9s12.3 13 21.3 13L448 224c8.8 0 16 7.2 16 16c0 6.8-4.3 12.7-10.4 15c-7.4 2.8-13 9-14.9 16.7s.1 15.8 5.3 21.7c2.5 2.8 4 6.5 4 10.6c0 7.8-5.6 14.3-13 15.7c-8.2 1.6-15.1 7.3-18 15.1s-1.6 16.7 3.6 23.3c2.1 2.7 3.4 6.1 3.4 9.9c0 6.7-4.2 12.6-10.2 14.9c-11.5 4.5-17.7 16.9-14.4 28.8c.4 1.3 .6 2.8 .6 4.3c0 8.8-7.2 16-16 16H286.5c-12.6 0-25-3.7-35.5-10.7l-61.7-41.1c-11-7.4-25.9-4.4-33.3 6.7s-4.4 25.9 6.7 33.3l61.7 41.1c18.4 12.3 40 18.8 62.1 18.8H384c34.7 0 62.9-27.6 64-62c14.6-11.7 24-29.7 24-50c0-4.5-.5-8.8-1.3-13c15.4-11.7 25.3-30.2 25.3-51c0-6.5-1-12.8-2.8-18.7C504.8 273.7 512 257.7 512 240c0-35.3-28.6-64-64-64l-92.3 0c4.7-10.4 8.7-21.2 11.8-32.2l5.7-20c10.9-38.2-11.2-78.1-49.4-89zM32 192c-17.7 0-32 14.3-32 32V448c0 17.7 14.3 32 32 32H96c17.7 0 32-14.3 32-32V224c0-17.7-14.3-32-32-32H32z"/></svg> alerts and notifications

- How can data **hurt**? <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M323.8 477.2c-38.2 10.9-78.1-11.2-89-49.4l-5.7-20c-3.7-13-10.4-25-19.5-35l-51.3-56.4c-8.9-9.8-8.2-25 1.6-33.9s25-8.2 33.9 1.6l51.3 56.4c14.1 15.5 24.4 34 30.1 54.1l5.7 20c3.6 12.7 16.9 20.1 29.7 16.5s20.1-16.9 16.5-29.7l-5.7-20c-5.7-19.9-14.7-38.7-26.6-55.5c-5.2-7.3-5.8-16.9-1.7-24.9s12.3-13 21.3-13L448 288c8.8 0 16-7.2 16-16c0-6.8-4.3-12.7-10.4-15c-7.4-2.8-13-9-14.9-16.7s.1-15.8 5.3-21.7c2.5-2.8 4-6.5 4-10.6c0-7.8-5.6-14.3-13-15.7c-8.2-1.6-15.1-7.3-18-15.2s-1.6-16.7 3.6-23.3c2.1-2.7 3.4-6.1 3.4-9.9c0-6.7-4.2-12.6-10.2-14.9c-11.5-4.5-17.7-16.9-14.4-28.8c.4-1.3 .6-2.8 .6-4.3c0-8.8-7.2-16-16-16H286.5c-12.6 0-25 3.7-35.5 10.7l-61.7 41.1c-11 7.4-25.9 4.4-33.3-6.7s-4.4-25.9 6.7-33.3l61.7-41.1c18.4-12.3 40-18.8 62.1-18.8H384c34.7 0 62.9 27.6 64 62c14.6 11.7 24 29.7 24 50c0 4.5-.5 8.8-1.3 13c15.4 11.7 25.3 30.2 25.3 51c0 6.5-1 12.8-2.8 18.7C504.8 238.3 512 254.3 512 272c0 35.3-28.6 64-64 64l-92.3 0c4.7 10.4 8.7 21.2 11.8 32.2l5.7 20c10.9 38.2-11.2 78.1-49.4 89zM32 384c-17.7 0-32-14.3-32-32V128c0-17.7 14.3-32 32-32H96c17.7 0 32 14.3 32 32V352c0 17.7-14.3 32-32 32H32z"/></svg> obsessive checking/tracking

---

- What’s the purpose of knowing how many emails one sends?

- Do parents ever show data patterns to their kids?

- Would this even help them self-regulate?

---

- Who **owns** the data? The interpretation?

- **Bias** exists in human interpretation and is baked into data analytics

---

# <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M80 160c0-35.3 28.7-64 64-64h32c35.3 0 64 28.7 64 64v3.6c0 21.8-11.1 42.1-29.4 53.8l-42.2 27.1c-25.2 16.2-40.4 44.1-40.4 74V320c0 17.7 14.3 32 32 32s32-14.3 32-32v-1.4c0-8.2 4.2-15.8 11-20.2l42.2-27.1c36.6-23.6 58.8-64.1 58.8-107.7V160c0-70.7-57.3-128-128-128H144C73.3 32 16 89.3 16 160c0 17.7 14.3 32 32 32s32-14.3 32-32zm80 320a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Module 2 <br> Final Thoughts?

---

---

---

**Measure <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Collect <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Analyze <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Report**

---

**(Performance at School)**

---

Measure <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Collect <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Analyze <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Report

### <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M80 160c0-35.3 28.7-64 64-64h32c35.3 0 64 28.7 64 64v3.6c0 21.8-11.1 42.1-29.4 53.8l-42.2 27.1c-25.2 16.2-40.4 44.1-40.4 74V320c0 17.7 14.3 32 32 32s32-14.3 32-32v-1.4c0-8.2 4.2-15.8 11-20.2l42.2-27.1c36.6-23.6 58.8-64.1 58.8-107.7V160c0-70.7-57.3-128-128-128H144C73.3 32 16 89.3 16 160c0 17.7 14.3 32 32 32s32-14.3 32-32zm80 320a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> **What might we measure?**

---

- How did student learning changed when doing school from home, if at all?

- With whom do students interact in class discussions?

- From whom do teachers seek professional advice?

---

<div class="caption">
<p>TechTrends article: "The History and Development of Learning Analytics in Learning, Design, & Technology Field" (<a href="https://doi.org/10.1007/s11528-022-00720-1" target="_blank">Ye, 2022</a>)</p>
</div>

---

---

**Online Class Discussion - Social Network Analysis**

```r
edgelist1 <- 
  read_csv("data/meteakca_edgelist.csv", 
           show_col_types = FALSE) %>%
  mutate(group = week,
         week = substr(week, 1, 1)
         ) %>% 
  group_by(from, to) %>% 
  mutate(weight = n())
glimpse(edgelist1)
```

```
## Rows: 317
## Columns: 5
## Groups: from, to [182]
## $ week   <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
## $ from   <chr> "AL", "VW", "RN", "CH", "KW", "KJ", "Kwi", "AM", "KW", "KW", "Z…
## $ to     <chr> "RN", "RN", "KW", "RN", "CH", "CH", "CH", "KW", "AM", "AL", "AL…
## $ group  <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
## $ weight <int> 1, 1, 1, 1, 2, 3, 1, 2, 2, 2, 1, 2, 1, 1, 1, 3, 1, 2, 1, 1, 1, …
```

---

**Online Class Discussion - Social Network Analysis**

```r
head(edgelist1, 15)
```

```
## # A tibble: 15 × 5
## # Groups:   from, to [15]
##    week  from  to    group weight
##    <chr> <chr> <chr> <chr>  <int>
##  1 1     AL    RN    1          1
##  2 1     VW    RN    1          1
##  3 1     RN    KW    1          1
##  4 1     CH    RN    1          1
##  5 1     KW    CH    1          2
##  6 1     KJ    CH    1          3
##  7 1     Kwi   CH    1          1
##  8 1     AM    KW    1          2
##  9 1     KW    AM    1          2
## 10 1     KW    AL    1          2
## 11 1     ZT    AL    1          1
## 12 1     LW    AL    1          2
## 13 1     RS    AL    1          1
## 14 1     SP    AL    1          1
## 15 1     JK    AL    1          1
```

---

**Online Class Discussion - Social Network Analysis**

```r
sociogram1 <-
  tidygraph::as_tbl_graph(edgelist1) %>%
  mutate(popularity = centrality_degree(mode = 'in')) %>%
  ggraph(layout = 'kk') +
  geom_edge_arc(alpha = .2, 
                width = .5, 
                strength = .5,
                color = 'steelblue'
  ) +
  geom_node_point(alpha = .4, aes(size = popularity)) +
  scale_size(range = c(1,10)) +
  theme_wsj() + 
  scale_colour_wsj("colors6") +
  theme(axis.line=element_blank(),
        axis.text.x=element_blank(), axis.text.y=element_blank(),
        axis.ticks.x =element_blank(), axis.ticks.y =element_blank(),
        axis.title.x=element_blank(), axis.title.y=element_blank(),
        panel.background=element_blank(), panel.border=element_blank(),
        panel.grid.major=element_blank(), panel.grid.minor=element_blank())
```

---

**Online Class Discussion - Social Network Analysis**

### <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M416 0C352.3 0 256 32 256 32V160c48 0 76 16 104 32s56 32 104 32c56.4 0 176-16 176-96S512 0 416 0zM128 96c0 35.3 28.7 64 64 64h32V32H192c-35.3 0-64 28.7-64 64zM288 512c96 0 224-48 224-128s-119.6-96-176-96c-48 0-76 16-104 32s-56 32-104 32V480s96.3 32 160 32zM0 416c0 35.3 28.7 64 64 64H96V352H64c-35.3 0-64 28.7-64 64z"/></svg> **Next step:** Look at the network by group or by week

---

**Online Class Discussion - Social Network Analysis**

There are quite a few descriptive measures of networks:

- **Order:** number of nodes/vertices (students, in this case)

- **Size:** number of edges/connections (responses, in this case)

- **Reciprocity:** mutuality

- **Transitivity:** clustering

- **Diameter:** similar to degrees of separation

- **Density:** out of all possible connections, percentage that have been made

- **Node degree:** number of connections

- **Sentiment score:** how positive or negative in aggregate

- Character count, Word count, Length of threads

---

**Online Class Discussion - Social Network Analysis**

Analyze the network:

```r
graph1 <- tidygraph::as_tbl_graph(edgelist1)
```

- **Order:** 23

- **Size:** 317

- **Reciprocity:** 0.5874587

- **Transitivity:** 0.6530769

- **Diameter:** 4

- **Density:** 0.6264822

- **Node degree:** 26

---

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

**Article:** [A social network perspective on peer supported learning in MOOCs for educators ](http://www.irrodl.org/index.php/irrodl/article/view/1852) (Kellogg, Booth, & Oliver, 2014)

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

**Data source:** [Massively Open Online Course for Educators (MOOC-Ed) network dataset](https://dataverse.harvard.edu/dataset.xhtml;jsessionid=9ad052693563b29056a88d490182?persistentId=doi%3A10.7910%2FDVN%2FZZH3UB&version=&q=&fileTypeGroupFacet=&fileAccess=&fileSortField=name&fileSortOrder=desc)]

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

```r
edgelist2 <- 
  read_csv("data/DLT1 Edgelist.csv", 
           show_col_types = FALSE) %>%
  group_by(Sender, Receiver) %>% 
  mutate(Weight = n()) %>%
  ungroup() %>%
  relocate(Sender, Receiver, Weight)
  
glimpse(edgelist2)
```

```
## Rows: 2,529
## Columns: 11
## $ Sender                  <dbl> 360, 356, 356, 344, 392, 219, 318, 4, 355, 355…
## $ Receiver                <dbl> 444, 444, 444, 444, 444, 444, 444, 444, 356, 4…
## $ Weight                  <int> 1, 2, 2, 1, 1, 3, 2, 4, 1, 2, 4, 1, 1, 2, 1, 1…
## $ Timestamp               <chr> "4/4/13 16:32", "4/4/13 18:45", "4/4/13 18:47"…
## $ `Discussion Title`      <chr> "Most important change for your school or dist…
## $ `Discussion Category`   <chr> "Group N", "Group D-L", "Group D-L", "Group O-…
## $ `Parent Category`       <chr> "Units 1-3 Discussion Groups", "Units 1-3 Disc…
## $ `Category Text`         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ `Discussion Identifier` <chr> "Most important change for your school or dist…
## $ `Comment ID`            <dbl> 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
## $ `Discussion ID`         <chr> "2", "1", "3", "4", "5", "6", "6", "2", "3", "…
```

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

```r
head(edgelist2, 15)
```

```
## # A tibble: 15 × 11
##    Sender Receiver Weight Timestamp    `Discussion Title`  `Discussion Category`
##     <dbl>    <dbl>  <int> <chr>        <chr>               <chr>                
##  1    360      444      1 4/4/13 16:32 Most important cha… Group N              
##  2    356      444      2 4/4/13 18:45 Most important cha… Group D-L            
##  3    356      444      2 4/4/13 18:47 DLT Resources—Comm… Group D-L            
##  4    344      444      1 4/4/13 18:55 Most important cha… Group O-T            
##  5    392      444      1 4/4/13 19:13 Most important cha… Group U-Z            
##  6    219      444      3 4/4/13 19:16 Most important cha… Group M              
##  7    318      444      2 4/4/13 19:26 Most important cha… Group M              
##  8      4      444      4 4/4/13 19:44 Most important cha… Group N              
##  9    355      356      1 4/4/13 20:12 DLT Resources—Comm… Group D-L            
## 10    355      444      2 4/4/13 20:13 Most important cha… Group D-L            
## 11      4      444      4 4/4/13 20:14 DLT Resources—Comm… Group N              
## 12    310      444      1 4/4/13 20:34 Submitting Questio… Unit 1 Expert Panel  
## 13    248      444      1 4/4/13 20:42 Most important cha… Group D-L            
## 14    150      444      2 4/4/13 21:42 Most important cha… Group O-T            
## 15     19      310      1 4/4/13 23:13 How important is t… Group A-C            
## # ℹ 5 more variables: `Parent Category` <chr>, `Category Text` <chr>,
## #   `Discussion Identifier` <chr>, `Comment ID` <dbl>, `Discussion ID` <chr>
```

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

Analyze the network:

```r
graph2 <- tidygraph::as_tbl_graph(edgelist2)
```

- **Order:** 442

- **Size:** 2529

- **Reciprocity:** 0.1997544

- **Transitivity:** 0.0888077

- **Diameter:** 8

- **Density:** 0.0129744

- **Node degree:** 4

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

```r
sociogram2 <-
  graph2 %>%
  mutate(popularity = centrality_degree(mode = 'in')) %>%
  ggraph(layout = 'kk') +
  geom_edge_arc(alpha = .2, 
                width = .5, 
                strength = .5,
                color = 'steelblue'
  ) +
  geom_node_point(alpha = .4, aes(size = popularity)) +
  scale_size(range = c(1,10)) +
  theme_wsj() + 
  scale_colour_wsj("colors6") +
  theme(axis.line=element_blank(),
        axis.text.x=element_blank(), axis.text.y=element_blank(),
        axis.ticks.x =element_blank(), axis.ticks.y =element_blank(),
        axis.title.x=element_blank(), axis.title.y=element_blank(),
        panel.background=element_blank(), panel.border=element_blank(),
        panel.grid.major=element_blank(), panel.grid.minor=element_blank())
```

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

---

**Massive Online Open Course (MOOC) Discussion - Social Network Analysis**

```r
dlt1_no <- 
  read_csv("data/DLT1 Nodes.csv", show_col_types = FALSE)
glimpse(dlt1_no)
```

```
## Rows: 445
## Columns: 13
## $ UID         <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
## $ Facilitator <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ role1       <chr> "libmedia", "classteaching", "districtadmin", "classteachi…
## $ experience  <dbl> 1, 1, 2, 2, 3, 1, 2, 1, 1, 2, 3, 3, 2, 1, 3, 1, 1, 1, 3, 1…
## $ experience2 <chr> "6 to 10", "6 to 10", "11 to 20", "11 to 20", "20+", "4 to…
## $ grades      <chr> "secondary", "secondary", "generalist", "middle", "general…
## $ location    <chr> "VA", "FL", "PA", "NC", "AL", "AL", "SD", "BE", "NC", "NC"…
## $ region      <chr> "South", "South", "Northeast", "South", "South", "South", …
## $ country     <chr> "US", "US", "US", "US", "US", "US", "US", "BE", "US", "US"…
## $ group       <chr> "UZ", "DL", "OT", "N", "AC", "AC", "OT", "AC", "N", "N", "…
## $ gender      <chr> "female", "female", "female", "female", "female", "female"…
## $ expert      <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"…
## $ connect     <chr> "1", "0", "1", "0", "0", "1", "0", "0", "0", "0", "1", "0"…
```

(try using **[brms](https://paul-buerkner.github.io/brms/)**: An R package for Bayesian multilevel models using Stan)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M64 32C28.7 32 0 60.7 0 96V416c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V96c0-35.3-28.7-64-64-64H64zM224 224a32 32 0 1 1 0 64 32 32 0 1 1 0-64z"/></svg> <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M0 96C0 60.7 28.7 32 64 32H384c35.3 0 64 28.7 64 64V416c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V96zM352 352a32 32 0 1 0 -64 0 32 32 0 1 0 64 0zM128 192a32 32 0 1 0 0-64 32 32 0 1 0 0 64z"/></svg> <br><br> **Learning Analytics** <br><br> Comparing Examples 1 & 2

---

---

<table class="table table-striped table-bordered" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:center;"> Variable </th>
   <th style="text-align:center;"> Example 1 (Classroom) </th>
   <th style="text-align:center;"> Example 2 (MOOC) </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:center;"> Order </td>
   <td style="text-align:center;"> 23.0000000 </td>
   <td style="text-align:center;"> 442.0000000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Size </td>
   <td style="text-align:center;"> 317.0000000 </td>
   <td style="text-align:center;"> 2529.0000000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Reciprocity </td>
   <td style="text-align:center;"> 0.5874587 </td>
   <td style="text-align:center;"> 0.1997544 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Transitivity </td>
   <td style="text-align:center;"> 0.6530769 </td>
   <td style="text-align:center;"> 0.0888077 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Diameter </td>
   <td style="text-align:center;"> 4.0000000 </td>
   <td style="text-align:center;"> 8.0000000 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Density </td>
   <td style="text-align:center;"> 0.6264822 </td>
   <td style="text-align:center;"> 0.0129744 </td>
  </tr>
  <tr>
   <td style="text-align:center;"> Median Degree </td>
   <td style="text-align:center;"> 26.0000000 </td>
   <td style="text-align:center;"> 4.0000000 </td>
  </tr>
</tbody>
</table>

---

---

- **Module 1:** Introduction to Analytics
- **Module 2*:** Performance Analytics
- **Module 3: Learning Analytics**
- **Module 4:** Web Analytics
- **Module 5:** Data Visualization
- **Module 6:** Ethics in Learning Analytics
- **Module 7:** Future of Analytics
- **Module 8:** Case Discussions

---

- <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 0 48 256a208 208 0 1 0 416 0zM0 256a256 256 0 1 1 512 0A256 256 0 1 1 0 256zm306.7 69.1L162.4 380.6c-19.4 7.5-38.5-11.6-31-31l55.5-144.3c3.3-8.5 9.9-15.1 18.4-18.4l144.3-55.5c19.4-7.5 38.5 11.6 31 31L325.1 306.7c-3.2 8.5-9.9 15.1-18.4 18.4zM288 256a32 32 0 1 0 -64 0 32 32 0 1 0 64 0z"/></svg> Explore

- <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M88.2 309.1c9.8-18.3 6.8-40.8-7.5-55.8C59.4 230.9 48 204 48 176c0-63.5 63.8-128 160-128s160 64.5 160 128s-63.8 128-160 128c-13.1 0-25.8-1.3-37.8-3.6c-10.4-2-21.2-.6-30.7 4.2c-4.1 2.1-8.3 4.1-12.6 6c-16 7.2-32.9 13.5-49.9 18c2.8-4.6 5.4-9.1 7.9-13.6c1.1-1.9 2.2-3.9 3.2-5.9zM0 176c0 41.8 17.2 80.1 45.9 110.3c-.9 1.7-1.9 3.5-2.8 5.1c-10.3 18.4-22.3 36.5-36.6 52.1c-6.6 7-8.3 17.2-4.6 25.9C5.8 378.3 14.4 384 24 384c43 0 86.5-13.3 122.7-29.7c4.8-2.2 9.6-4.5 14.2-6.8c15.1 3 30.9 4.5 47.1 4.5c114.9 0 208-78.8 208-176S322.9 0 208 0S0 78.8 0 176zM432 480c16.2 0 31.9-1.6 47.1-4.5c4.6 2.3 9.4 4.6 14.2 6.8C529.5 498.7 573 512 616 512c9.6 0 18.2-5.7 22-14.5c3.8-8.8 2-19-4.6-25.9c-14.2-15.6-26.2-33.7-36.6-52.1c-.9-1.7-1.9-3.4-2.8-5.1C622.8 384.1 640 345.8 640 304c0-94.4-87.9-171.5-198.2-175.8c4.1 15.2 6.2 31.2 6.2 47.8l0 .6c87.2 6.7 144 67.5 144 127.4c0 28-11.4 54.9-32.7 77.2c-14.3 15-17.3 37.6-7.5 55.8c1.1 2 2.2 4 3.2 5.9c2.5 4.5 5.2 9 7.9 13.6c-17-4.5-33.9-10.7-49.9-18c-4.3-1.9-8.5-3.9-12.6-6c-9.5-4.8-20.3-6.2-30.7-4.2c-12.1 2.4-24.7 3.6-37.8 3.6c-61.7 0-110-26.5-136.8-62.3c-16 5.4-32.8 9.4-50 11.8C279 439.8 350 480 432 480z"/></svg> Discuss

- <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M64 64C28.7 64 0 92.7 0 128V384c0 35.3 28.7 64 64 64H512c35.3 0 64-28.7 64-64V128c0-35.3-28.7-64-64-64H64zm16 64h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM64 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V240zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16zm80-176c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V144zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V240c0-8.8 7.2-16 16-16zM160 336c0-8.8 7.2-16 16-16H400c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V336zM272 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H272c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM256 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H272c-8.8 0-16-7.2-16-16V240zM368 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H368c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM352 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H368c-8.8 0-16-7.2-16-16V240zM464 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM448 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V240zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16z"/></svg> Create

---

### Discussion (50 points)

- Initial post on Flip due by second Friday (but post as early as possible)

- At least 3 response due on Flip by end of Module 3 (second Sunday night)

---

### Analytics Problem Plan (100 points) - **due end of Module 3**

- "Write a brief report (approximately 750-1000 words) identifying and describing a real-world problem that might be addressed via analytics."

- "The emphasis of this assignment is on the conceptualization of the problem and the clear identification of a possible data source."

- "Then briefly describe how analytics will be used to solve the problem"

- "You will not need to collect actual data related to this problem, just plan for how it would be done."

---

<hr>

**What questions can I answer for you now?**

**How can I support you this week?**

<hr>

---

# Learn to Code

**https://datascienceineducation.com/**

---

# <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M181.5 197.1l12.9 6.4c5.9 3 12.4 4.5 19.1 4.5c23.5 0 42.6-19.1 42.6-42.6V144c0-35.3-28.7-64-64-64H128c-35.3 0-64 28.7-64 64v21.4c0 23.5 19.1 42.6 42.6 42.6c6.6 0 13.1-1.5 19.1-4.5l12.9-6.4 8.4-4.2L135.1 185c-4.5-3-7.1-8-7.1-13.3V168c0-13.3 10.7-24 24-24h16c13.3 0 24 10.7 24 24v3.7c0 5.3-2.7 10.3-7.1 13.3l-11.8 7.9 8.4 4.2zm-8.6 49.4L160 240l-12.9 6.4c-12.6 6.3-26.5 9.6-40.5 9.6c-3.6 0-7.1-.2-10.6-.6v.6c0 35.3 28.7 64 64 64h64c17.7 0 32 14.3 32 32s-14.3 32-32 32H384V336 320c0-23.7 12.9-44.4 32-55.4c9.4-5.4 20.3-8.6 32-8.6V240c0-26.5 21.5-48 48-48c8.8 0 16 7.2 16 16v32 16 48c0 8.8 7.2 16 16 16s16-7.2 16-16V204.3c0-48.2-30.8-91-76.6-106.3l-8.5-2.8c-8-2.7-12.6-11.1-10.4-19.3s10.3-13.2 18.6-11.6l19.9 4C576 86.1 640 164.2 640 254.9l0 1.1h0c0 123.7-100.3 224-224 224h-1.1H256h-.6C132 480 32 380 32 256.6V256 216.8c-10.1-14.6-16-32.3-16-51.4V144l0-1.4C6.7 139.3 0 130.5 0 120c0-13.3 10.7-24 24-24h2.8C44.8 58.2 83.3 32 128 32h64c44.7 0 83.2 26.2 101.2 64H296c13.3 0 24 10.7 24 24c0 10.5-6.7 19.3-16 22.6l0 1.4v21.4c0 1.4 0 2.8-.1 4.3c12-6.2 25.7-9.6 40.1-9.6h8c17.7 0 32 14.3 32 32s-14.3 32-32 32h-8c-13.3 0-24 10.7-24 24v8h56.4c-15.2 17-24.4 39.4-24.4 64H320c-42.3 0-78.2-27.4-91-65.3c-5.1 .9-10.3 1.3-15.6 1.3c-14.1 0-27.9-3.3-40.5-9.6zM96 128a16 16 0 1 1 0 32 16 16 0 1 1 0-32zm112 16a16 16 0 1 1 32 0 16 16 0 1 1 -32 0z"/></svg> <br><br> Play in the <br> [Analytics Sandbox](https://bretsw.com/sandbox)