Where Do Great Ideas Go to Die?
People have great ideas all the time that they never share with others. They secretly harbor them in their heads. This is often where they die. We’re not always given a platform to share ideas, so that’s part of the reason. Another is we often feel our ideas might not be well received, so why bother. Or change is just hard for some and doing things the way we’ve always done them is commonplace. I tend to lean more on the side of people will listen if you bother to seek out the opportunity even if change never happens.
I have what I think is a good idea, and I’m going to share it with you. I don’t have any expectations for change, but at least my great idea is not going to die in my head. Also note, this post was conceived before our current situation with moving courses online. I started this while on Spring Break.
I’ve been teaching online for a long time – since 1998. I can see an inherent problem with how we offer online classes for our students. We open classes. Students rush to fill them, and all the online classes are full weeks before the semester begins. Sounds great, right? Well, it’s not. Not every student who signs up for an online class is prepared and ready for an online class. Many never make it past the first few days, finding it difficult to follow simple directions and get work completed. What do we do with these students? Some drop on their own, others stay and struggle for a while and eventually drop. The end game is that often after just one week a once full class is now left with multiple open spots. These are missed opportunities for students who were never given a chance to even register.
So here’s my idea. Open all online courses 3 days early and require students to complete an orientation. If students “No Show” or can’t complete simple to-do items, they are dropped as a “No Show” from the class. They were given an opportunity and failed. The student gets a full refund and there is now an open spot for another student to enroll. But we don’t allow late registration, so that doesn’t work. However, if we designated some courses as “rolling overload.” I made that term up. It means that faculty can designate the number of overload students permitted to enroll in their online courses. Presently faculty can teach an online course that doesn’t have the required max number of students (15) and are compensated from a rolling payscale, meaning I can teach ENH114 if I only have 10 students enrolled if I’m willing to be paid a certain percentage of the full load. That number used to be 2.04 load for 10 students. Five students would be 1.08 load. These are just examples at this point based on old numbers.
With this new plan, faculty could designate the number of overload students they are willing to teach, and the load for that class would increase by the number. Then after the three day period where students are given the orientation to complete, the actual course load is determined. Here’s the example: I teach ENG101 with a course load of 24 students. I designate 10 open spots for overload (2.04), so initially, my new full-time load is 15 + 2.04 = 17.04. After the three day orientation period, I only have 29 of the 34 students successfully make it through. My new load is 15+1.08 (5 extra students). We have technically helped 10 students. Five were shown they were not adequately prepared for an online class and were given a refund, and five more were given the opportunity to take a class that previously would have been full and closed. And I am compensated for the extra students in my class.
So let’s look at some real numbers, and I’ll show why I know this will work. For the last 5+ years, I’ve been keeping track of students enrolled during the first two weeks of my online classes. This semester I have 5 online classes. The two online 8-week ENG101 classes ended last week, and two new ENG102 online 8 week classes began this week. I already knew that at least 3 of the students enrolled in the ENG102 courses were not eligible to take the class, but I couldn’t drop them from the ENG102 because the semester wasn’t over yet for the ENG101. They hadn’t officially failed ENG101 yet, but trust me; they failed. So there were 3 wasted spots already. By the time all the official stuff happened, we are already in the no late registration stage. But let’s focus on the two ENG101 courses. I started with 48 students and I ended with 34. After the first week, I had a total of 43 students. So 5 enrollments were lost within the first 3 days. Most of the other 9 students were lost within the next two weeks.
Here’s the best part. I can predict after one week which students will not succeed in the online course. As they complete the 7 step orientation, I rank them in order of how quickly and successfully they complete the orientation. The names at the top completed it quickly with very little difficulty. Names toward the bottom are students who didn’t get started right away, required several emails to prod them, and didn’t complete things in a successful manner. The majority of the 9 students who dropped or were dropped after the first week were at the bottom of this list. Only 3 students in the top 32 have dropped or been dropped from the class, while the bottom 7 have either dropped or are failing the course.
Now let’s look at what is happening right this minute in my two ENG102 courses. The orientation was due last night. Both classes were full before we started. I add one off the waitlist and 2 students from my previous ENG101 that just ended, so I started with 51. One disappeared right when I opened the class on Wednesday of Spring Break. Poof. Vanished. Down to 50. Today a week later, three days into the 8-week session, I have 44 students. What happened to those 6 students? Two more dropped on their own. One said she had too much going on to handle a new class right now. Three were complete no-shows. I emailed daily and then called to no responses. They were dropped with a 43 (no-show) this morning. The last was a difficult decision but he was dropped with a 43 because he couldn’t figure out how to complete the orientation and never responded to any of my emails or texts offering help.
So even with all the intervention I still ended up for 4 open spots that didn’t get filled for this 8-week session. I bet there are a lot of students out there right now that wished they’d just signed up for an online class. But it’s too late now, as those 44 students are already deep into the course discussing personal freedoms and learning about writing arguments. Anyone who tried to join now would be too far behind for it to be a fair challenge. The system is just not designed well enough to give more students the opportunity to take online courses. Who knows if my idea would work. It’s certainly not without flaws. It’s just an idea, and now that it’s not dead in my head, I’m good with letting it go. Fly away idea. 🙂
And Write6x6 is a wrap. I hope you enjoyed my brain dumps over the past 6 weeks. I’ll try not to wait until next year to post again.
Sabbatical 2018 Week 10: What Happened to Weeks 7-9?
Boy, time sure flies when you’re busy. It’s already week 10. I had to go back to August to count the weeks because I barely know what day it is, let alone how many weeks have passed in the semester. This of course is all good. While on sabbatical, I’ve also been renovating a vacation home we purchased up in Happy Jack, so my life has been consumed with data and renovations for the last three months. Thankfully one of those projects is almost complete. And that would not be the data project. On we roll.
Big Data is still my world at the moment. I’m currently in course 5 of the Big Data Specialization on Coursera. Course 5 is Graph Analytics for Big Data. I’m learning about how real world data science problems can be modeled as graphs along with various tools and techniques. The biggest thing I’ve learned so far is that most people don’t know what graphs are. Most people think graphs are these pretty pie charts.
These are not graphs apparently. These are pie charts. I knew that. I love pie charts. We are not learning how to make pie charts in the Graph Analytics for Big Data course. We’re learning how to make this below. This is a graph with nodes and edges.
I should have know this was not going to be simple. This graph theory is tied to math, so they are “mathematical structures used to model pairwise relations between objects.” “Graphs can be used to model many types of relations and processes in physical, biological, social and information systems” (Wikipedia).
A good example of how graphs can be used is with fraud detection. Graph databases are uniquely positioned to spot the connections between large data sets and identify patterns, a useful trait when it comes to spotting complex, modern fraud techniques. A better example is the product recommendations you get on Amazon and other online retail sites. Amazon can pull together product, customer, inventory, supplier and social sentiment data into a graph database to spot patterns and make smarter recommendations to you.
I’m still wrapping my head around how graphs can be useful in education. For an assignment I designed a graph around a peer review assignment for students. It’s pretty basic, but in my mind this might be useful data to find patterns to help students improve their work.
Later in this course we will be learning how to use Neo4j, a graph database management system and GraphX, Apache Spark’s API for graphs and graph-parallel computation. So I imagine my graphs in another week will be much better.
Next post I’ll share some information about Canvas Data Portal, as I now have access to Maricopa’s instance. It’s so exciting even though I don’t really know how to “look” at the data yet, but I can see all the flat files. I just need a database to magically appear with a data scientist attached to help. 🙂
Sabbatical 2018 Week 4: Where’s My Money?
It has become painfully clear that I will never be a data analyst. That’s not necessarily a bad thing considering I already have a job as an educator at a great community college. Thank goodness for that because I’m a little over my head here in my Big Data Specialization from the University of California San Diego. Somehow I’m learning just enough to get by, but don’t ask me anything specific. You really have to be a programmer to use this stuff.
Course 2 was Big Data Modeling and Management Systems and it was very technical. It was all about Big Data technologies, and frankly I’m happy to leave that part to the IT experts. Systems and tools discussed included: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL <eyes glass over>. We learned an in-depth knowledge of why big data modeling and management is essential in preparing to gain insights from your data, and knowledge of real world big data modeling and management use cases in areas such as energy and gaming. We also learned about different kinds of data models, the ability to describe streaming data and the different challenges it presents, and the differences between a DBMS and a BDMS.
I some how managed to complete the final assignment for this course, which was to design a data model for a fictitious game: “Catch the Pink Flamingo.” The strangest thing about this whole Coursera setup is the assignments are peer reviewed. I’m awaiting my fate as I type. I wasn’t really clear if what I was doing was correct, but I did my best and submitted the assignment. Then I had to go in and review my classmates’ work. Yeah, right? It looked good. Nothing like mine, but hey, who’s right? I guess we’ll see once my assignment is peer reviewed.
Two courses down; four to go. Then on to the Johns Hopkins Data Science Specialization. In the mean time, I’ve reached out to our district IT person in charge of Canvas. I’m hoping to meet with her soon to discuss Canvas Data Portal. ITS has a proposal process when our resources are needed for more than 20 hours, so I have to go to the PMO site which is where a business case can be initiated to start the process. Additionally, the IITGC provides prioritization of business cases/projects for ITS, so I’ll have to cross my fingers and hope my case gets prioritized.
Okay, back to figuring out how to get paid correctly. Hey, Maricopa, where’s my money?
Sabbatical: Supporting Data-Driven Decision Making With Educational Data Analytics Technologies
I’m happy to say that I was awarded a sabbatical for the 2018-2019 academic year. The fancy title of this post will be the focus of my sabbatical. It should be a grand ole dandy time, and I’m looking forward to spending my time doing and learning something new. If you’d like to read more about my sabbatical, I posted a few key points below.
Abstract: Learning analytics is a new and developing field. There is a growing literature base around learning analytics and its impact on student grades and retention. Although learning analytics is still at a relatively early stage of development, there is convincing evidence from early adopters that learning analytics will help to improve outcomes. It only makes sense that Maricopa would want to tap into this new field. Learning analytics has been defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Sampson, 2016). Maricopa with its use of Canvas LMS and SIS has an overabundance of data that goes unused. Becoming a data analysis authority will enable me, as a full-time faculty member, to help support data driven decision making at GCC using education data analytics technologies, which includes Canvas Data Portal.
Goal(s) – what the sabbatical will accomplish. A vital aspect of data driven decision making is Data Literacy for Teachers, which is the primary goal of this sabbatical, to empower myself to use data in the decision-making process, so that I can help support data driven decision-making at GCC using education data analytics technologies. Data Literacy for Teachers “comprises the competence set (knowledge, skills, and attitudes) required to identify, collect, analyze, interpret, and act upon Educational Data from different sources so as to support improvement of the teaching, learning and assessment process” (Sampson, 2016). Our LMS, Canvas, produces a lot of data that presently is not being used. By becoming a data analysis authority and more knowledgeable in Canvas Data, I will be able to help support other faculty and administrators with data driven decision making at GCC using these data analytics from Canvas.
Objectives – steps to achieve the goal(s). The objectives for this project mostly follow the competency set (knowledge, skills, and attitudes) required for Data Literacy for Teachers. They are required to identify, collect, analyze, interpret, and act upon Educational Data from different sources. There are several steps involved in this project.
- Identify and learn about big data, analytics and data analysis.
- Identify and learn about Canvas Learning Analytics.
- Learn about Canvas Data Portal.
- Learn how to collect the data from Canvas into various tools for analysis.
- Learn Data Analysis to discover what the right questions to ask will be.
- Learn how to interpret learning data to predict and influence outcomes (act upon).
- Assess and identify which BI Tools schools are leveraging to analyze data.
- Create/Find a collection of example queries that use Canvas Hosted Data to answer questions; queries that could be very useful to solve problems at GCC (act upon).
- Create awareness guides and a workshop for faculty on Canvas Learning Analytics.
- Create a resource guide for district CTL’s on Canvas Data Portal.
- Get Canvas Data Portal turned on in Maricopa.
The only objective I’m worried about not accomplishing is the last. It can be a challenge at time getting things with in Maricopa accomplished, but I’m up for the fight.