Welcome to Education Matters on Think Tech Hawaii. I'm your host today, Carol Monly. Our show is called HPU's Dual Credit Program, College Credit While in High School, with my guests, Manny Sagal, the Dean of HPU's College of Professional Studies, and Robert Wilson, HPU's instructor in the College of Professional Studies. Hawaii Pacific University has developed a dual credit course program for high school students up both on the Big Island and Oahu. Last year, HPU partnered with Honoka'a High School to offer college credit for two courses. Robert Wilson used video conferencing tools to teach writing and reading arguments and intro to communication skills from the HPU downtown Honolulu campus to high school students in Honoka'a. If you want to ask a question or make a comment, you can tweet us at thinktechhi or call us at 374-2014. Welcome to the show. Thank you. Thanks for having us. So good to have you here. I'm so particularly interested in this program because I think it's so valuable. So we'll start out with you, Manny, because you're the Dean and you're kind of the master behind all of these important programs. So tell us, how did it get started? Why is it important, and what are some of the problems and benefits in creating a dual credit program? Oh, good question, Carol. So when we take a look at something like this, we think that education empowers us for the future when it comes to us as an individual, taking a look at our families, taking a look at the community. And it started with actually President Gotanda, talking to the DOE, asking where there was a need and what we could do to help for a university. We're always taking a look at just contributing to society as well as what we can do here for the islands. And one thing led to another, and we were told that Honoka'a could use some help with some dual credit programs, some college credit courses. And it snowballed from there. So very exciting. Over the last year, we've been able to offer a couple of classes at Honoka'a. So what were the problems, what were they facing in Honoka'a, which of course is at the tip of the big island and not near Hilo or Kona or any other larger town or city? So I guess it was more geographical than anything else. Trying to, like you said, remote and trying to get another institution to come in and possibly teach a few classes there. It had started off a few years ago and it fizzled out. So another university, another college was there. I see. And so that led to an opportunity for us to, again, help out. So what we're talking about now are high school students and these must be students who are very high level in terms of wanting to aggressively learn more than just what the high school curriculum is offering. Is that right? Definitely, definitely. Very motivated. It's also taking a look at some students that possibly never thought that they would even attend college, go to college or just didn't even think about it, and to give them the opportunity to actually take a college course, see how it is, and hopefully decide that, hey, this is for me. It's doable. I can complete the course and continue on to an associate's or bachelor's degree. So does Honoka'a High School offer AP courses, advanced placement courses for college credit? Yes, they do. They do already. And so I was wondering at the beginning, why would we offer college courses when they have AP courses? Correct. Yeah. So what was the reason, why would they decide to do this? Well, a couple of things. AP courses are typically spread over the whole year and the college courses are one semester. So your courses are only offered one by semester. Correct. So, following Honoka'a's bell schedule and their day schedule, we're fitting their calendar rather than ours, but it's still a semester.  So again, why not AP courses? Well, in some places, AP courses, the pass rate is only 5%. Really? Well, you score at a certain level. You score at a certain level. I think at the 3, 4, 5 score is only attainable by 5% of the student. In some places, yeah. So it's lower than, let's say, a college course. Because a college course, it's shorter. We also depend on the school to select the students. We make final say, but we're asking the advisors for their recommendations. Who's ready academically and who's ready in terms of motivation? I see. So is there a test to get into the college level course that HPO offered? We have a placement test for writing, and we would have one for math too, but we rely mostly on their recommendation. Recommendation. I see. And so how many students, how big is Honoka High School, and how many students actually enrolled in your course? I think it's about 660, something like that, enrollment, not at Honoka. At Honoka. I think it's both the high school and middle school, or is that just the high school? It is intermediate also. I see. Honoka High and intermediate. I see. And so then you were able to establish a college credit course for both the fall and the spring. And did the students have to pay for it? They did not. We had a donor graciously set up the funds for us and able to pass it along to the students, and that included their textbooks as well. So there was no cost to any of the students who participated in the program. What a wonderful opportunity for these students to get college credit without paying for it, and then being able to transfer it to colleges. Is that right? That's right. Shout out to our generous donor as well. So tell us, how many credits was your course, and tell us about the course that you actually taught then at Honoka. Okay. The regular college courses, so three credit hours, which involves 40 hours of instruction, just like our main campus courses. The fall course was introduction to... It was freshman writing. Heavy dose of argument, formal argument of the writing. The school requested that, and then for the second term we offered communications, introduction to communication. That was, again, 40 hours of instruction. I was qualified to teach both, so I continued on from the fall to the spring. So mechanically tell us how that works. So you're here in Honolulu at the HPU campus in downtown Honolulu, which is right there, our background, the beautiful background of Loha Tower. And the students are all the way in Honoka in their classroom. Actually, I think we have a picture of Rob, what he sees, it's in the library, yes. So tell us what is that picture of? That's the students taking their final exam. And they're sitting in the library. And how many students? That's the first semester or the second? That's the first semester. They were 18 who signed up, one dropped within the first week just for scheduling conflicts. But all 17 made it to the end. And where are you in all of this picture? They're sitting there at their long tables quietly writing an exam and you're facing them on a screen? Actually, I'm there. Oh, you're physically there, that one you went to. Right, so the first day of the term and the last day. I see. Okay, great. But typically, all the other classes, four days a week, you said, was it 40 minutes a day? You're on a screen? I'm on a screen. I can make it as big, I can fill up my image on the screen. So they see me very well. I have limited ability to zoom in on them. So what you saw in that image of the class, I have to pretty much know who's who's sitting where because I can't see all the details of their face and whether they're understanding. I see. I can see whether they're looking down at their phones. So what kind of equipment are you using in long distance from here and what do they have there? Does that include purchase of equipment? Was there a need to purchase new equipment? We had some new equipment. Longer term, our tech person is looking to move it up a notch to make it more interactive so that the video can zoom. I can see them more. We can have more mics positioned around the room. We had one central mic so I can hear what these two students are whispering it to each other. Is there a camera person there in Honoka? They have the tech person there on standby.  Is he an HPU person or Honoka? No, he works in Honoka. So he's a tech person for the high school who helps sets it up and you are then remote here in Honolulu but live during the period. So they can see me. We both log in at the same time. I can log in before him or vice versa. We're on the same account basically. We use the software called Zoom. It's an online video conferencing software. We would like to upgrade in the future. The tech person has a very good idea of how we can make this more interactive. There are smart whiteboards that we can set up on both ends. So I can have a student on the classroom side write something on the whiteboard and it shows up on my whiteboard and vice versa. That way we wouldn't have to zoom in on the board to see what I'm writing or what they're writing. So Manny, is there a plan to acquire this kind of extension? There is. We've got an amazing CIO, Cody Down and his staff and team and Mark Nita who happens to be a former grad of Honolulu. So he's very familiar with the way things are and set up and he still has a lot of connections there. So now he works at HP with us under the CIO and so he's in the IT department and he has been amazing when it comes to being instrumental in setting things up and what we need and what we could do to improve things. So we're always looking at continuous improvement to make sure that there is a seamless flow when it comes to communication for the students and their instructor. And Rob in this case. Right. So I guess I'm interested in two things then. First the technology and then the type of the curriculum itself. So as far as the technology then, so this is really distance learning which many of us have known for a long time, right? Higher education is used, distance learning, different campuses and all that. So how does this differ from the distance learning that we've all heard about in the past? Typically distance learning is asynchronous. Which means that the students can access the material anytime they want. It doesn't have to be, they don't have to be online the same time as a teacher. And so the teacher is not actually live during the time, the period. I see. So we want it to be synchronous so we can see what they're. And they're all at the same time, the same place hearing you, hearing the same information and then they can hear each other's question. Yes and when I hear their question, everybody can hear my answer. So do you think that's a better way of learning for high school students? Definitely. Why is that? Well, let's say if we went to a podcast, if I just recorded the lecture and posted it. And each level courses are often like that. And then it turns into an online class that's asynchronous and it really cuts down the interactivity. And yes, you could use discussion board, you could use email, but I'd like rooms, chat rooms, yes. And online courses do use that and do use it successfully. But then the question is why gather the students in one place? And I think if we let the students go off one on one, doff on their own, they might not do it. Right. It's a completely different experience. Right. Okay. Now let's talk about the actual content of the courses. How did you find the students able to participate and to learn and to be equivalent of high school freshman, a college freshman? It was a challenge with the writing because the way I teach my course, it's more contemporary issues, topics, terrorism, educational reform. They don't have much real world experience beyond the high school level. So I had to adjust my topics to their interest and through their knowledge level. Of course, I assign articles that they can read that prepare them for the final. And the final is a problem solution essay. How should the U.S. government, for example, deal with this problem? Or educational reform? How should Hawaii change its education? Okay. Should high school be required? Right. Well, we're going to go to break. And after the break, I want to come back and ask you, how do the students do? Okay. Okay. So this is Education Matters with Think Tech, Hawaii. And my guest, Rob Wilson, and Manny Sakal from HP will be right back. I'm getting older. Do I need to worry about falling? Yes, you do. Each year, one in four people, 65 and older, will experience a fall. And many will be serious. The majority of falls happen at home, so remove things that could make you trip and install handrails to keep you steady. To learn more about the steps you can take to help prevent a fall, please talk to your doctor. You can also visit aarpfoundation.org or Medicaremadeclear.com slash falls. This message was brought to you by UnitedHealthcare and AARP Foundation. Hi, everyone. I'm Andrea Gabrieli. The host for Young Talent's Making Way here in Think Tech, Hawaii. We talk every Tuesday at 11 a.m. about things that matter to tech, matter to science to the people of Hawaii with some extraordinary guests, the students of our schools who are participating in science fair. So Young Talent's Making Way every Tuesday at 11 a.m. only on Think Tech, Hawaii. Mahalo. Welcome back. This is Karamann Lee with Education Matters. And my guest, talking about a dual-credit program established by Hawaii Pacific University and its different locations. So we ended the last first half by talking about the exam that you gave to the 17 Honokah High School students, and you had two classes, one in the fall and the spring. So how did they do taking a college-level class? The writing course in the fall, I was pleasantly surprised that they all finished. Not all passed. So we have pass rates that are very comparable to what we have on the main campus, real college classes. And what is that? What's the pass rate? For this one, I think it was about 70, 80 percent. Passed. Yes. Okay. And the second term, we had 10 students, and those 10 were actually continuing from the fall, the same students. So they got two courses. We had seven out of 10 pass. And how many credits did they get? Three credits for each. Three credits for each, which they can now transfer on their transcripts. That's right. So was also able to use for their high school graduation requirements? They were, actually, yes. Yes. So they get high school and college credit. Great. And they did not have to pay a penny for the course. They did not. Wonderful. What a great opportunity. I think we have a picture of the group of students. One more image. Okay. So Rob, tell us, this is from the first semester or the second semester? That was first semester, and I think that was the first day. I went over there for the first day of class and for the last day of class. I'm there on the right. And we're in the library building. I was going to say that's an old student on the right, but no, they should. Okay. Great. Okay. So let's talk, so that was, so tell me, how did you think it went? If you evaluated the program for the first year, some of the pros and cons of this dual credit program? Good learning on both sides. There are many things I would change to make it better. How would you, what would you change? Well, we start with talking with the advisor at Honokaw or whichever school we're at. How would you like us to treat the students? Do you want us to treat them just like normal college students? Give the assignment, let them go? Or do you want us to have more shepherding involved? And should that shepherding come from me, the instructor, or you, the advisor on your end? And what did you do this year, without knowing? Their decision was treat them like normal college students. And I think we could get a higher pass rate if the advisor had the permission to just step in and say, how are you doing? Are you keeping up in your homework? And I could keep the advisor apprised of what's going on. Who might need some little nudging or pushing or pulling? That seems to make sense, because you're acknowledging on one hand that they're intellectually capable of college of courses. But physically, emotionally, they still may be, they are high school students. Yeah, time management. So yeah. Okay. That's another good thing. If it was a face-to-face class on the main campus, and I saw a student who's falling behind and not turning in assignments, I would have the student come to my office. And I can't do that. Regardless of fact, they're already college students, right? Right.  And that's why people choose HP. Yeah. Okay. So I guess that you found that we're beyond what you had expected. Hmm. I think just the opportunity that we were able to provide the students Yes. was phenomenal. Yes. And just to know that we are able to help in a certain way to help ready our students of the future, to take a look at college. And again, to let them know that they're capable, they're ready. They have the opportunity. And if they would like to continue to take more dual credit courses, it actually helps with their family when it comes to the financial side of things going to college afterwards. Absolutely. Saving time and money. Exactly. Right? They're graduating in four years. They can maybe graduate in less. That's right. That's right. And they can, I understand that they actually feel now that they can manage college. Yeah. Exactly. Right? Have a sense that this is something that they can do. It's not. Can I do it? I have done it. Right. Yeah. So, okay, let's talk about the future now. So what's happening? Are you going to go back to Honoka? What are the schools you've been working with? So we love dealing with Honoka and we love the students at Honoka and with great experience, so we are ready to go back in the fall. And you have all the technology ready. We do. And we're constantly improving that as well. So we're looking right now at discussing with the principal and the counselor with regards to what courses their students would be interested in taking. And we're ready to offer that in the fall and we're trying to expand it actually to maybe even offer a couple of courses each semester. Just taking a look at logistics and how we would run things right now. Right. And what about Oahu? I know that there's some schools on Oahu. So yes, we have a partnership of five schools all together. So we also have St. Andrew's Schools right down the street from us downtown. We have Island Pacific Academy. We have Kamehameha Schools. And we also have Marinal. Wonderful. So have you already started some of those partnerships? We actually have started all of them all within the last couple of years. St. Andrew's Schools has been with us a long time affiliation. How does that work? It's not the same distance learning issues you have. Exactly. So with St. Andrew's Schools, currently it's the girls that are sophomores, juniors, seniors, they actually come to our campus and they'll take a course that was just regularly offered. And so they blend right in and regular college students and they take the course and they get credit for it. And again, they're able to take that anywhere they go or if they want to stay with us, they automatically get that college credit ahead of time. So helping them finish earlier, helping them financially, but also helping them complete their degree to a better degree earlier and then being, I guess, entering the workforce. Quicker. So do they have to apply to college first to be admitted to take these courses? Or they're still in high school? They're still in high school. So they'll fill out a special status form with us. And that allows them to take three to four classes with us at HPU without being enrolled. Without being enrolled. As a freshman. As a freshman, exactly. I see. And then tuition again. How is that paid for? A credit. So a good question. Each individual school is set up a little bit differently. So I would probably encourage if there's a student out there or a school out there to come and talk to us directly and we'll see what we can do. But there is no cost to the student. No cost to the student. No cost to the student. And so, and it's your regular curriculum. So if you have your writing course in place at HPU downtown campus, right? So you might have, how many students do you typically have in your class? Writing courses, I think, are capped at 18. 18. And of those you could have then some HPU, some priory, St. Andrew's Priory students, maybe a high school students, part of it. But you would still have some college students in there too. Yes. The first time we had the marinal students on campus, I was their instructor. It was an unusual situation. But we had them together in one class. So we had six students come in, five of them placed into my class in terms of writing. And then we had five other regular college students in that class. So it was a good mix. But now, in subsequent terms, they're all diffused throughout the regular section. So they're not kept together. Did you find, do they blend in pretty well then with the freshmen? They do. Sometimes the instructors have no idea that they're 16 or 17 years old. Right. And so are your exams blind exams? You know, do you know the name of the student who's writing an exam? Yes. Oh, OK. Yeah, our classes are so small. Yeah, it's hard to have blind. Yeah. Law schools always have blind exams. Yeah. That's right. And so what you said, do you have how many more schools? You have Honoka and you have all these other schools, local schools for this coming year. That's right. Yes, we do. The Pacific Academy is a little different where we actually hold the classes on their campus. I see. And they're open to HPU students as well as Island Pacific Academy students as well. How exciting. So you offer so many different programs with different schools. Is this something that other universities and colleges do regularly? Offer high school level, offer courses for high school students? U-H does. Uh-huh. They have, I think starting this fall, they have something called the Manoa Academy for different school students. I know that St. Francis, right across from U-H, they'll be able to send some students to Manoa Academy starting in the fall. The early start program, same idea. Great. And do you need funding? How is that working? How do you support? How does HPU, is this part of your regular day to day budgeting? So what we're looking at right now again is each school is done individually, but we try and make sure that nothing is passed on to the students. So this is strictly a complete benefit for the student and from textbooks right on to taking the course, they save on the tuition. So really to encourage all students to take a look at the capabilities that are within and take the chance of taking that college course and getting a chance to do it before you actually go off the college or if you're staying right on island, getting that opportunity, experience beforehand. So you feel comfortable when you actually do enter college. And so you have to work closely then with each school to, they actually select or take care of the enrollment of the students, is that right? Right now they do yes. They make sure that the respective school hand selects their students and then we also, once we get the students, we'll do a double check and make sure everything's good and that they have the proper prerequisites and we want to make sure we set up our students for the success. Exactly. Exactly. And how do you measure success then? I mean we know the pass rate for Honoka maybe next year, if you did more hands on with the students it might even be higher. But how else would you measure success? Is this a program that's growing for HPU? We are hoping it does, we're anticipating it does and we are just taking a look at giving back whatever we can to the islands. Yeah. To Oahu and the neighbor islands. And... How about any other neighbor islands? Possibilities are open. Yeah. We're excited the way things have been going so far. So nothing but positive and we are looking to again continue and expand. An example with Merinol, the students at Merinol come to HPU full-time. So they send us some of their best and brightest. They go full-time at HPU during the day and in the afternoon they go back to Merinol for their religion class and their sports and clubs. So at the end of two years they would have their high school diploma and an associate's degree. Oh my goodness. And they haven't paid any HPU tuition. That's amazing. So they say $50,000. Two full years. Wow.  Well this time has gone by so fast so I mean just kind of we only have a few seconds left and Manny I'm going to let you look in the camera for it and just tell our audience or viewers how they can contact you in case they have more questions. Whether it's schools, administrators or students. Yeah. So you can actually shoot me an email. It's msigal at hpu.edu and I think it's on the screen right now.  And we could probably get this turned around within a few months so that your students could take advantage of taking the college credit classes as soon as possible. Great. Well thank you so much Rob. Thank you so much Manny. It has been a really important topic I think. It's something that I think our viewers should be very interested in especially in this economic time and in this time where students have high levels of interest and opportunity to actually take college level courses. Thank you. Thank you. Thank you. Well that brings us to the end of our show today. We've enjoyed bringing it to you. I'm your host Carol Monly and we've been talking about HPU's dual credit program, college credit while in high school with Manny Segal and Rob Wilson. If you want to see this show again go to thinktechhawaii.com or youtube.com slash thinktechhawaii where there will be a link to this show and many more just like this one. As you may know thinktechhawaii is a 501C3 Hawaii non-profit digital media company dedicated to raising public awareness about issues and events that affect our lives together in these islands. To keep us going we need support from our underwriters and viewers just like you. Please help us by making a donation on our homepage at thinktechhawaii.com Aloha and we'll see you next time. Thank you.


Hey, this is Brett, and this 2014 Ford F450 Crew Cab Long Box Dual Rear Wheel Lariat Ultimate FX4 is stock number 14131Z. I am here at Summit Automotive in Fond du Lac, Wisconsin, you're new and used heavy duty truck headquarters. This 2014 Ford F450 comes with the 6.7 liter power stroke diesel engine. It's paired up with a six-speed automatic transmission. This truck has been fully safety to end inspected by our service shop per the state of Wisconsin inspection process. It has a fresh oil and filter change, all the fluids have been checked and topped off, and this truck is 100% ready to go. Super clean for a 2014, and I'm going to go all the way around in this video, inside, underneath, start it up and take a look under the hood, show you all the options, and just give you the most accurate representation that I can of the vehicle. Lexedo Black Metallic is the color, I show all my videos in 4K, if you like the video consider subscribing to the YouTube channel, click the bell notifications and get updates on the videos I do each and every day, as well as having access to one of the largest catalogs of vehicle and heavy duty truck walk-arounds on YouTube. This one does come with these 17 inch polished aluminum wheels, and it has BF Goodrich rugged trail TA tires on here. These are LT24575R17s, I would say in the front here we have probably about 60 to 70% of the tread left on those tires. Comes with the Rancho shocks, frame and under bodies in pretty nice shape. This is a clean title history, clean Carfax 2 owner out of Virginia and Ohio. Front fenders in pretty nice shape, headlight lenses are nice and clear, does come with the factory fog lights, front bumpers in excellent condition, no dents or dings on there. Those have the chrome tow hooks and the lower valance is absolutely perfect. You also get the chrome trimmed grille, the Ford logo is in nice shape, and the hood is in excellent shape as well, I didn't see any major dents or dings on there. Passenger side front fender is in really nice condition too. Passenger side front wheel, other than just a little bit of the aluminum oxidizing, nothing too bad on there, no major scuffs or scrapes. Tuxedo black has got a lot of metal flake in it, a lot of colors, a lot of purples, blues, yellows, it's a cool color. As you go down this side of this 2014, take note of how clean the body is, how reflective and mirror like that paint is. I take these HD videos so if you're far away or even if you're close by and you just cannot make the trip down but you're still interested in purchasing the vehicle, you can see the truck here, the truck, and have confidence in what you're looking at before you even get here. So when you do get here, there's absolutely no surprises and you can make a smart and informed buying decision from wherever you're at. If this video helps you make that buying decision, let your salesmen know that you saw the video, that it was helpful and that Brett sent you. Down that side, very nice and clean. The box side here and dually tub all look pretty good. There's a little scratch right there, nothing too bad. Back wheel is about the same condition as rest, just a little bit of oxidization there. Back tires have just as much tread as the front tires. You do get the camper package with the anti-sway bars right there. And underneath you get all the remaining factory exhaust. Looks pretty good for a 2014. Lower rockers and cab corners, I didn't see any corrosion on those. And it does come with some factory chrome step bars. Some of the chrome is peeling just a little bit. Coming around to the back of the vehicle, you get the FX4 decal there, the chrome-tipped exhaust. I didn't see much damage to the dually tub. Box side looks really good. Coming around to the back, rear bumper is in great shape. No major dents or dings on there. It does have the backup parking sensors, full towing package, which includes the receiver hitch forepin and seven pin wiring. The tailgate is in fantastic condition. You do get a tailgate step assist on here. Comes with the fifth wheel and gooseneck prep package as well as a gooseneck hitch. You do get seven pin wiring back here. And the bed's got a little bit of light duty usage, a couple of little dings and stuff. Nothing too bad. Nothing you wouldn't expect on a F450. Tailgate shuts nice and solidly. And as we go down on the driver's side, just as clean as that passenger side, no major dents or dings. On the box here, dually tubs in pretty nice shape. And for full disclosure, this back wheel, about the same condition as the rest. Down the rest of this side of the truck, cab and doors are all in really nice condition. This one does have the power scope mirrors. They power fold in, power telescope out. They have built-in directional signals and they're heated. I'll show you how those work in just a second. It does have the driver's side door code injury. We do have the code for this truck. If you ever want to figure out what your code is for your 11 to 16 superduty in the upper right hand part of the screen, I've got a video showing you how to find that. Back seats are in fantastic condition. No rips or tears back here. Does have the power sliding rear window with the built-in rear defrost. Seats are in a really nice shape. They fold up. There is a lockable storage compartment right there. That locks right there with the ignition key.  You do get side curtain airbags on those front seats, child safety locks on the back doors and the inside and bottoms of the doors. All look very good. I didn't see any corrosion on them. Show you the VIN sticker. No Canadian trucks here. And the tire and loading information sticker as well. Inside the Lariat Ultimate package gives you the black leather seats. 40, 20, 40 split bench seating, no rips, no tears on those seats. They are in fantastic condition. Smells very clean inside this truck. You get the weather tech floor mat in front, auto headlamps, power pedals, tilt, telescopic steering wheel, power windows locks and mirrors, Sony premium sound system, memory driver's seat, and these mirrors do power fold in. I always like showing both sides so you know that both sides are working properly. And then the ignition has to be on for the telescopic feature to work. So we'll hop inside, check out the miles, radio, everything that this one asked to offer as well as those telescopic tow mirrors. First off you can see this one has 69,446 miles instrument clusters, very nice and clean. On that digital display comes with the leather wrapped steering wheel, Bluetooth and audio controls on the right, cruise controls and information center controls on the left and those mirrors do power telescope out and in, and once again both sides out and in. This one has the sync eight inch radio, the six speed automatic transmission with the optional tap shift there, has the factory navigation system, there is highway 41's, so that is working nicely.  This is also where your backup camera shows up and you can see that is working nicely. Right here is your CD player, turned out for a drive, traction control system, dual climate controls, more tactile volume and tune controls as well, factory brake controller, four auxiliary up-fit switches, 110 volt, 150 watt plug-in, nice wood grain trim on the dash, the dash is in very nice clean condition. And a passenger side format and seat are in excellent condition, no rips or tears on there. Once again, headliners in really nice shape, it does come with the power sunroof and home link buttons for your garage door security systems and lighting systems, map lights and your power sliding rear window button. Let's start it up, take a look under the hood. Starts right up, you get the built forward tough animation there, no check engine lights or anything like that. I would personally like to thank you for checking out the video today and hopefully from this HD video you've been able to verify the quality condition options and of course cleanliness of this truck all the way around. All those lights are working nicely as well as the cab lights up top. And under the hood we have the 6.7 liter power stroke diesel engine, engine bay is very clean, one's very smooth. Once again this truck has been fully safe and inspected by our service shop, has a fresh oil and filter change, all the fluids have been checked and topped off. Shocks are doing a nice job holding that hood up, there is the emissions sticker. And this truck is 100% ready to go. I would highly recommend this truck from a quality and condition standpoint, I think whoever is going to get it is going to get a lot of use out of it and be very happy with it. If you want to see more pictures of this truck or one of our other 550 new and used cars, trucks, SUVs, minivans, wranglers, halftones, three quarter tons, one tons, F-250s, F-350s, F-450s, F-450s, you name it, we got to go to the website right there, summitauto.com full pictures and descriptions of every single vehicle, all at summitauto.com. If you'd like to check out more HD videos, keep consider going to youtube.com slash summitauto. Click the bell notifications, get updates on the videos they do each and every day as well as having access to one of the largest catalogs of vehicle and heavy duty truck walkarounds on YouTube. 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Good afternoon, everyone. Welcome to this afternoon session. I'd like to introduce Lorena Mesa. She's a platform engineer at Sprout Social in Chicago, and she's a Star Trek fan, and she's going to talk to us about spam and natural language processing. So real fun fact, I have a loud voice. Is this too strong or should I be a little quieter? Louder? Oh, this is great. I can be loud. Alright, thank you so much for joining me tonight. This afternoon, I should say the name of this talk is is that spam in my ham? Subtext, a novice's inquiry into classification. So as my announcer already said, my name is Lorena Mesa, and as you can see, I'm a huge Star Trek fan, so live long and prosper. Apart from that, I'm here from Chicago. A little bit about me and why I wanted to chat on this topic. I'm actually a career changer. So a few years ago, I came from being a data analyst in the social, in the social science space. Specifically, I worked at Obama for America doing data governance, and I then switched into doing software engineering about three years ago. Some big questions that were driving me at the time are captured in this talk, but some other things I do. I love Django Girls. I helped with the workshop yesterday. It's a glorious, glorious thing. If you have the opportunity to mentor, please do. If you would like to sign up for another one, please do it as well. Pie Lady Chicago is a group that I founded in Chicago, and I recently was voted to the board of directors for the Python Software Foundation, which is very exciting. So I'm going to chat a little bit about this great experience that we've all had before. I think we might have all had some kind of email at some point in time where we get something that flutters into our inbox, and it has language like de-junk and speed up your slow PC. And of course, we would trust an email that comes from aol underscore member info at emails. Yes, with a Z on it. aol.com. And of course, I'm going to trust anything that tells me this is free. This is great. You really should do it. So I think when we see emails like this, we know visually just by looking at it that it's a piece of spam. We know it's junk. We don't care about it. We ignore it. So how do we move from saying I know it when I see it to saying I can programmatically detect what a piece of spam is by using Python? So in today's chat, we're going to be thinking about three questions. One, what is machine learning? Two, how is classification a part of this world? And three, how can I use Python to solve a classification problem like spam detection? This chat is going to be really focused on a beginner understanding of machine learning. So if you are looking for more intermediate and advanced talks, I definitely know this would be a great conference to check out some of that. But we're going to really be taking this from the lens of a beginner. So machine learning, if you were to follow the emojis on the left-hand side, the top left would be me. Confused. Not sure what machine learning is. I'm like, is it a robot? Is it Johnny 5? Johnny 5 being a superhero from a children's movie I loved when I was a little kid, who's super quirky, can arch their eyebrows and come save the day. Well, I don't really think machine learning is Johnny 5. So let's go ahead and think a little bit about what machine learning is. One of the things I like to do when I begin working in a new problem space is I try to find some language to actually gravitate myself to understand what types of problems I will be solving. If I were to look around for some language defining machine learning, I might find something like this. Some discussions saying that there's pattern recognition, computational learning, artificial intelligence, what's going on? I don't know what that is. But there is a part of this that does make sense to me. The study of algorithms that can learn and make predictions on data. I like data. I like algorithms. Tell me more. So I think a better way we can think about machine learning is to borrow some language from Tom Mitchell, the chair of machine learning department at Carnegie Melling. He wrote machine learning, which is kind of a quintessential text for folks who want to start learning about machine learning. And he says we can think about machine learning in three kind of parts. We can say a computer program is said to learn from experience e with respect to some task t and some performance measurement p if its performance on t as measured by p improves with experience e. Okay, so we have a task. We have experience. We have a performance measurement. I can do this. This makes sense to me. So when I think about experience and how do I know what I know?  I I have memories stored up that teach me things about what I like, what I don't like, what I should do, what I shouldn't do. So maybe as a kid and I was a very hyperactive child, I would be running around like a maniac all the time because I had to be super fast.  You might be klutzy. You might fall and skin your knee. How many times do you have to skin your knee in elbows? For me, it probably took quite some time for me to learn I shouldn't run around like a maniac. I should walk around like a normal person so I don't hurt myself. That pain was a teaching experience for me. Likewise, when my grandmother was in the kitchen making tamales because I love tamales, I would always trying to be sticking my hand on the stove and more than once I definitely burned my hand. The idea of putting your hand on red hot coils, not very smart. So over time I learned to recognize that as a sign. I shouldn't do that. So when we think of experience as a human, we may think of our memories. What does that mean in different problem spaces? If I were to ask the question, what is the historical experience of the stock market? Well, I could say if I want to understand what a piece of stock has done historically, I might go look at what the records tell me about the price of that stock two years ago on July 17th, one year ago on July 17th. And you know, depending on how far back I want to do some analysis, I have historical data that can tell me something about the historical performance of that stock. So we have human memories. We have some memories there, but maybe in other spaces again, we want to go to historical data that can teach us something. So coming to machine learning and classification, what does experience actually mean? Let's frame this in Mitchell's framework. Our first problem is going to be identifying a task. For us, we want to classify a piece of data. So our question is, is an email spam or ham? And the idea here of ham is just anything that's not spam. It's cute. It rhymes. So spam or ham, that's our task, our experience. We're going to have a set of labeled training data. Essentially, what does that mean? We have a collection of emails and we have a label that is saying that the email is either ham or spam. So we have a collection of emails that we already know is one thing or the other. And then our performance measurement is the label correct. So what we need to do is be able to verify if emails are indeed spam or ham. So thinking about a classifier that we can use, we can think of naive Bayes. Naive Bayes is a type of probabilistic classifier. I love this image because I really want to know who's the person that has a neon light of the Bayes theorem, like in their office or in their front window. I don't know who that person is, but I applaud you. You are really great. So naive Bayes comes to us from stats theory. It's based on the Bayes theorem, no surprise. One of the key things with the Bayes theorem is when we talk about the likelihood of events, the key thing here to note is that we treat events as independent of one another. That's where the naive assumption comes from when we say we're going to be using a naive Bayes classifier. So for those of us who may not remember exactly what it means when we talk about independent and dependent events, let's have a quick refresher. So if I was going to ask you what's the probability of flipping a quarter six times in a row and getting heads, how would you go about solving that problem? Well, let's think about it on the first flip. I have two outcomes. I have heads or tails. So the likelihood of getting heads is going to be 0.5. The second time I flipped that, 0.5. Third time and so forth is going to be 0.5. So the likelihood of flipping a quarter and receiving multiple heads in a row is going to be independent of one another. So when we talk about independent events, we're trying to think of the outcomes. In contrast to dependent events, let's say we're talking about horse number five on the, I guess, your right-hand side. If my question was, what's the likelihood that horse number five is going to win the big derby, one of the things I would say is, well, we need to think about what are the weather conditions? Is it rainy? Is it sunny? Perhaps we want to think about the age of the horse, the health of the horse. There can be other things that are tied up in the likelihood of horse number five winning. So in this context, the probability of horse number five winning is going to be dependent on other things. For example, the weather. So when we talk about naive bays, our assumption is we have independent events. So when we talk about emails, we're really going to be thinking about the words that make up the emails. So let's think about these words. If I was going to say what's the likelihood of the word messy appearing with the word Barcelona, we're going to assume that there's no relationship. That's what naive bays tells us to do. Even though in our heads, we might think that there's a relationship. Or back to some really spammy language we love, what's the relationship between by and now? We're going to assume that there is no relationship. That the likelihood of by is not going to be impacting the likelihood of now appearing in a corpus of words for an email. So naive bays and spam classifiers, again, our question is what is the probability of an email being ham or spam? So the base theorem here in the middle, we've got three things we need to kind of think of. One, what's the likelihood of the predictors in the class? Two, the prior probability of the class. And three, the prior probability of the predictor. All of these together will help us compute the aposteriori probability of a class. So when I say class, our class is here, ham, spam. Those are the only two classes we have. Our predictors are going to be the words in the email itself. So for example, if I'm looking at a piece of content and I say, okay, well, what's the likelihood of a predictor being in the spam or ham class, we can say if I'm looking at the word free, we can think of it as, well, 28 out of 50 spam emails have the word free. We will do this for each word in our email and we will find the likelihoods of all the predictors and multiply them together. We also then need to consider the prior probability of the class. So given the entire collection of data we're looking at, how many of them are of one class and how many of another? So for spam, if we have 150 emails we're working with, we can say 50 of those documents are spam, so 50 out of 150. And then the prior probability of the predictor, we're here saying, well, how many times has the word free appeared in all of our emails, let's say it's 72 out of 150 and there you go. So the Bayes theorem is basically frequency tables. How many times has this thing appeared? How many times has it appeared in the class? How many times has this class appeared in the collection of things that we're looking at? Great. We've made some calculations. We found some values between zero to one. How do we know which one to pick? Pretty easy. Whichever one has the higher maximum a posteriori probability. So the reason why we would say a posteriori here is we're not looking at anything new. We're looking at historical data, things that have already happened. Once we've made a calculation for class ham and for class spam, we simply just pick the larger of the two and we say this email is going to be either ham or spam. Pretty simple. So why naive Bayes? Well, I think just walking through this, we can arrive at an answer. It's pretty straightforward. It's as simple as frequency tables. I think we can all do this together. It may seem a little bit daunting at first, but once you start realizing the application of it, you can see that it's pretty straightforward. So for the context of if you are starting to think about classifiers and problems you want to start looking at, I would say this is a great one to start with. The math is accessible. And while you can use other algorithms, we will talk about some of the limitations in a moment, this is a good one to start with. So that's great. But how do I use Python to detect spam? Okay. Well, I cheated a little bit. I didn't do all my own data collection and munging and cleaning. As fun as that is, I instead went to find a data source out there that already was cleaned and labelled for me and where did I get it? I got it from Cagle in the classroom. So this is a website that has competitions. So the classroom component is more of their teaching problems. They have open competition problems as well. But I loved that my data was cleaned and labelled and I could just get right to work building a thing. So in our example here, our training data has 2,500 emails, 1,721 of them which are labelled one as ham and the balance labelled as spam, which is zero. So the labels themselves are just in a CSV. We have an ID and we have the prediction, zero or one, pretty straightforward. And the, that's a little grainy, I apologize. But the emails themselves are collections of text with some HTML in it. So what are we going to use when we write our very, very simplistic naive Bayes spam classifier? We're going to use these three things. We're going to use email. It's going to go ahead and parse our emails into message objects. We're going to use LXML because as I said, those emails have some HTML embedded in it. And right now, all I care about is the words themselves. So I want to strip that stuff out. And then we'll use NLTK natural language tool kit. And that's going to help us to filter out stop words. So let's go ahead and get to it and train the spam filter. So the training of the Python naive Bayes classifier, when I say train, we're going to go through these steps. The first thing we're going to do is we're going to tokenize the text. We will explain that in just a moment. One thing I do want to say is when we look at all the corpus of words in an email, I am not treating words like shop and shopping as the same word. You can actually do that. That's called stemming. So that would be like a bonus feature. I encourage you to go try that on your own. So I didn't do that for this example. So we're going to go ahead. We're going to tokenize our words, which we're going to do that for each email that we process. We want to then keep track of the unique words that we see of all the documents that we process. This will come into effect to help us with zero word frequencies. We are going to then increment the word frequency for each category. So our category is here being ham or spam. We're going to increment the category count, which again is that prior probability of the classes that we need to take into account. And then we're also just going to keep a track of how many words are in each category. And it's good to know how many training examples we've actually processed. So that's the last step. So training is pretty much going to start with this. Tokenizing text into a bag of words. That's what it is. It's a bag of words. So essentially, this is very simplistic. I've kind of trimmed it down a little. What we want to do is we want to pull out the words. This is already after we've removed the HTML that's embedded. And we're going to say, hey, for each word in our text, let's go ahead, lowercase the word. We're going to say, if it's a word, because why not? And we're going to say, as long as this word isn't in the corpus of stop words for the English language, let's go ahead and keep it. So stop words are words like the and or words that may appear often but may not provide us a lot of value in thinking about if this thing is going to be spam or not. So you can get that from NLTK. I'm glad I didn't have to compile that. We go ahead. We do this for each email. And now we have a bag of words. So remember that zero word frequency thing I was talking about? Well, let's think about this. So I've done my training and I have a new email. In this email that I'm looking at that I'm trying to classify, I have the word free, but problem. I've never historically have seen the word free in the spam collection of emails that I've looked at. So what's going to happen when I calculate the likelihood of all my predictors? I'm going to get zero. So to offset that, what we can do is we can add a small constant, like which Laplace smoothing permits us to do, and that allows us to have a small offset so that it doesn't throw our math out the window. So let's talk about classifying. All right. So this is a giant wall of text. But I just wanted to point out that it's quite literally iterations, countings, dictionaries. That's all this is. There is no black box magic here. Essentially what we do in the classifies, we say for each category that we're going to create this aposteria probability, we want to go ahead, find the probability of all the predictors. We want to then multiply that by the prior probability of the classes itself. And we're going to pick the one that has the higher value and that's what we classify the email as. Not very magical. So in the predictors probability, if we see something we haven't seen before, we're going to go ahead and then add a value of one to that. And this point right here about floating point underflow, when you are doing computations where you really care about having very precise decimal points, you're going to need to use specific objects. You could use a log instead. But in this case, I use decimal objects. And there is a note here, which you probably can't read. I will share these slides, which comes from the Stanford natural language processing description about how to handle doing a floating point computation. And they said use decimal. So that's what I went with. So okay, performance measurement. I've classified. I've picked a thing. How do I know how well I did? Okay, so I go ahead. My detector says let's train and evaluate. What I eventually come out with is I have 223 that are correct, 27 incorrect. My performance measurement is about 89%. As a small footnote, the idea of about 90% accuracy I believe is a benchmark. We obviously can do better here and we'll talk about what doing better can mean in a moment. So the idea of how to split up our training data. Let's do a 90-10 split. It's pretty much what I've seen as a standard. I'm sure given different problem spaces, you might want to chunk things up differently, but I went with a 90-10 split. Essentially, all I did was say, hey, on 90% of my data, let's go ahead and classify. Let's go ahead and train, that is. And then on 10%, we're going to go ahead and classify. And how do we know if the thing is incorrect or correct? Well, whatever label we ultimately assigned it, check that labels.csv, see if it's correct, see if it's incorrect, and it's basically straight math. So that's how we got the 89%. So some things to watch out for. False positives. Oh, this is really fine. So for example, Google does things really well, right? They do really good with spam filtering, but even they can have some flaws. So I do actually like to sign up for pedagonia emails, and this email was actually flagged a spam. So basically a false positive is when something is incorrectly identified, right? So you can run into this. So we can say, well, when something is incorrect, what's the problem? Is it that our implementation, because we're talking about naive base, is it too naive? One way we can also correct this, we can tell Google and say, hey, this is actually not spam. So I can actually validate the data and send it to them, and they can put it into their implementation and try to auto correct for that in the future. So false positives are a thing to watch out for. And some limitations with naive base and some challenges. Obviously this independence assumption is very, very simplistic. If I get a marketing email about Barcelona and they aren't talking about messy, I'm going to be very confused. Granted, there are some talks about him being traded, so we shall see. But obviously this independence assumption is quite simplistic. It is not the way that things work in the real world. What are the side effects of that? Well, one of the things is then we're going to go ahead and overestimate the probability of the label ultimately selected, meaning we're going to create more binaries. We're going to say it's either more to left or more to the right and how it aligns with the category label. And also we can think about this, remember how I said I cheated and I didn't go and label on my own data? Well, here's the other thing, human error. This type of algorithm, classifiers, are called supervised learning. They require historical labeled sets of data to go ahead and learn from in order to make predictions. Well, human error can be prone in this data process. What happens if, let's say I'm a professor and I'm making use of all my student lackeys and some of them have been up all night and 10 of them looked at the same email and they all came up with different labels for it, but it's in my training set. That's going to be very inconsistent. So I need to think about that as well. How is the labeling of the data happening? So as much as we don't like to think about data munging, data cleaning and data collection, that's actually a really important part of the process when working with machine learning problem, supervised machine learning problems. So how can we improve our performance? Well, we can do more and better feature extraction because while I would like to say that emails can only be identified by the words in them, we know that's not true. Predicting sentiment of emails is very complicated, very difficult, natural language processing is a huge field. I'm not getting into that myself, but we need to think about other ways we can identify spam. So what are some things? Perhaps the subject. Is there something weird in the subject I can pay attention to? What about the images? Is there an abundance of images in spammy emails? Or maybe there's none. I don't know. How about the sender? Remember that really cool email address with the Z in it? Because clearly I would trust AOL emails, whatever that was. Then again, I don't trust most AOL stuff. So that's another thing. But some other ones, if we were just going to think about what to go ahead and consider other possible features, we can think about capitalization, irregular punctuation, things like that. Ultimately, we also want more data. So do you like data on Star Trek and have more? Want to learn more? Go to Kegel. They're super sweet. I also would highly recommend Sarah Guido's introduction to machine learning with Python. She's a great data scientist at Bit.ly. And I've heard great things about this. And also your local friendly Python user group. We love talking. We love learning together. Talk to people here. There's a great talk after this, talking more about machine learning. Stay for it. So thanks. And if anything, I hope what you may have learned is correlation may be causation or causation may be correlation I don't know. So we can implement a thing, but the question then comes to how do we interpret those results? And that's where I challenge you to go ahead and try some things out. Thank you so much, y'all. Any questions? I did such a great job. No one has any questions. Do you have questions? I'll be hanging out in this area out here for a few minutes, but like I said, I do want to hear the next talk. So I'll be around. My name is Lorena. Please reach out and say hi. It's a pleasure to be here. Thank you so much for listening.