1 00:00:00,000 --> 00:00:04,000 You know how we're always talking about making sure people get the right training, like finding the right fit. 2 00:00:04,000 --> 00:00:05,000 Right, right. 3 00:00:05,000 --> 00:00:07,000 Well, this organization Automation Workz. 4 00:00:07,000 --> 00:00:09,000 It's a tech training setup. 5 00:00:09,000 --> 00:00:11,000 And they kind of crack the code when it comes to admission. 6 00:00:11,000 --> 00:00:13,000 Check the code, huh? I like it. 7 00:00:13,000 --> 00:00:20,000 You know, kidding, they can actually predict, like with 89% accuracy, whether someone's going to be successful in their program. 8 00:00:20,000 --> 00:00:21,000 Wow, 89%. 9 00:00:21,000 --> 00:00:22,000 That's unheard of. 10 00:00:22,000 --> 00:00:23,000 Right. 11 00:00:23,000 --> 00:00:26,000 And get this, they figure this out before the training even begins. 12 00:00:26,000 --> 00:00:27,000 Okay. 13 00:00:27,000 --> 00:00:28,000 Now I'm really intrigued. 14 00:00:28,000 --> 00:00:29,000 How do they do that? 15 00:00:29,000 --> 00:00:31,000 Yeah, that's the big question, right? 16 00:00:31,000 --> 00:00:33,000 So that's what we're going to unpack today. 17 00:00:33,000 --> 00:00:37,000 We're going deep on automation works is super powered admissions process. 18 00:00:37,000 --> 00:00:38,000 All right. 19 00:00:38,000 --> 00:00:39,000 Let's do it. 20 00:00:39,000 --> 00:00:41,000 So where do we start? What's the secret sauce? 21 00:00:41,000 --> 00:00:46,000 Okay. So first things first, they have this thing called the Admissions Sampler Workshop. 22 00:00:46,000 --> 00:00:47,000 Sampler workshop. 23 00:00:47,000 --> 00:00:50,000 Yeah. Basically, it's like a 90-minute orientation session. 24 00:00:50,000 --> 00:00:53,000 It gives potential students a taste of what Automation Workz all about. 25 00:00:53,000 --> 00:00:56,000 You know, they're approach to learning that kind of community they built. 26 00:00:56,000 --> 00:00:57,000 That sort of thing. 27 00:00:57,000 --> 00:00:59,000 Okay. Makes sense. A little taste test before you commit. 28 00:00:59,000 --> 00:01:00,000 Exactly. 29 00:01:00,000 --> 00:01:02,000 But here's where it gets really interesting. 30 00:01:02,000 --> 00:01:06,000 After that workshop, they give you homework, a whole seven days of it. 31 00:01:06,000 --> 00:01:07,000 Seven days. 32 00:01:07,000 --> 00:01:11,000 That's a bit more than your average, re-chapter one kind of homework. 33 00:01:11,000 --> 00:01:12,000 What's involved. 34 00:01:12,000 --> 00:01:14,000 It's way more than just textbook stuff. 35 00:01:14,000 --> 00:01:19,000 This homework is all about self discovery and figuring out if tech is really your jam. 36 00:01:19,000 --> 00:01:20,000 You know? 37 00:01:20,000 --> 00:01:23,000 Okay. I'm all ears. Tell me more about this unconventional homework. 38 00:01:23,000 --> 00:01:25,000 Well, one of the things they have you do is use this app. 39 00:01:25,000 --> 00:01:27,000 It's called Life Culture Audit. 40 00:01:27,000 --> 00:01:29,000 Life Culture Audit. What's that all about? 41 00:01:29,000 --> 00:01:34,000 So basically, it helps you visualize your dream life after you've completed the training. 42 00:01:34,000 --> 00:01:36,000 It's like creating a vision board. 43 00:01:36,000 --> 00:01:41,000 But with a focus on how your career sits into your ideal lifestyle. 44 00:01:41,000 --> 00:01:42,000 Hmm. 45 00:01:42,000 --> 00:01:43,000 I like that. 46 00:01:43,000 --> 00:01:47,000 Helping people connect their personal goals with their career aspirations makes a lot of sense. 47 00:01:47,000 --> 00:01:49,000 Right. It's not just about can you code. 48 00:01:49,000 --> 00:01:52,000 It's about where will you thrive as a coder? 49 00:01:52,000 --> 00:01:53,000 Yeah. 50 00:01:53,000 --> 00:01:54,000 Absolutely. 51 00:01:54,000 --> 00:01:56,000 Because coding skills alone aren't enough. 52 00:01:56,000 --> 00:02:00,000 You need to be passionate about the work and see how it fits into the bigger picture of your life. 53 00:02:00,000 --> 00:02:01,000 Totally. 54 00:02:01,000 --> 00:02:02,000 And to gauge your potential. 55 00:02:02,000 --> 00:02:05,000 You know, see if you've got that coder's instinct. 56 00:02:05,000 --> 00:02:08,000 They have you play this game called Senseii Games puzzle maze. 57 00:02:08,000 --> 00:02:09,000 Wait. 58 00:02:09,000 --> 00:02:11,000 They have a game as part of the admissions process. 59 00:02:11,000 --> 00:02:12,000 Yeah. 60 00:02:12,000 --> 00:02:16,000 It's this cool, gamified way to assess your aptitude for coding. 61 00:02:16,000 --> 00:02:19,000 But without the stress of a traditional test. 62 00:02:19,000 --> 00:02:23,000 Smart. That takes the pressure off and makes the whole experience more engaging at best. 63 00:02:23,000 --> 00:02:24,000 Exactly. 64 00:02:24,000 --> 00:02:25,000 Who doesn't love a good game? 65 00:02:25,000 --> 00:02:27,000 And then, to round things out. 66 00:02:27,000 --> 00:02:29,000 They also use the big five personality test. 67 00:02:29,000 --> 00:02:30,000 Ah. 68 00:02:30,000 --> 00:02:32,000 The ocean test. Right. 69 00:02:32,000 --> 00:02:37,000 Openness, conscientiousness, extroversion, agreeableness, neuroticism. 70 00:02:37,000 --> 00:02:38,000 That's the one. 71 00:02:38,000 --> 00:02:43,000 This gives an insight into your personality and how it might align with different roles in the tech world. 72 00:02:43,000 --> 00:02:44,000 Right. 73 00:02:44,000 --> 00:02:49,000 So if someone's super organized and detail oriented, high on that conscientious scale, 74 00:02:49,000 --> 00:02:57,000 they might be steered towards a role like software testing or data analysis, where those skills are really valuable. 75 00:02:57,000 --> 00:02:58,000 Exactly. 76 00:02:58,000 --> 00:03:00,000 It's about finding the right fit. 77 00:03:00,000 --> 00:03:04,000 You know, not just shoving everyone into the same coding boot camp and hoping for the best. 78 00:03:04,000 --> 00:03:06,000 I'm seeing a pattern here. 79 00:03:06,000 --> 00:03:11,000 It's all about personalization and making sure people are set up for success from the start. 80 00:03:11,000 --> 00:03:12,000 Absolutely. 81 00:03:12,000 --> 00:03:16,000 And that's how they get that 89% accuracy in predicting success. 82 00:03:16,000 --> 00:03:21,000 All this data from the Vision board app to the personality test, it all gets fed into their system. 83 00:03:21,000 --> 00:03:23,000 Okay. So they're gathering all this information. 84 00:03:23,000 --> 00:03:25,000 But how does it translate into a prediction? 85 00:03:25,000 --> 00:03:27,000 It's not magic, right? 86 00:03:27,000 --> 00:03:28,000 No, definitely not magic. 87 00:03:28,000 --> 00:03:31,000 They're most likely using some pretty advanced data analysis techniques. 88 00:03:31,000 --> 00:03:39,000 Uh, so they've got algorithms working behind the scenes, crunching the numbers and identifying patterns that indicate a high likelihood of success. 89 00:03:39,000 --> 00:03:40,000 Exactly. 90 00:03:40,000 --> 00:03:43,000 And this is where things get really interesting, ethically speaking. 91 00:03:43,000 --> 00:03:47,000 You know, they're always concerned about using personal data for these kinds of predictions. 92 00:03:47,000 --> 00:03:49,000 For sure, it's a sensitive area. 93 00:03:49,000 --> 00:03:53,000 But in this case, you could argue that it's being used for a positive purpose. 94 00:03:53,000 --> 00:03:58,000 I mean, if it helps people avoid investing time and money in a program, that's not a good fit for them. 95 00:03:58,000 --> 00:03:59,000 Right. 96 00:03:59,000 --> 00:04:06,000 It's like better to know upfront if you're not cut out for coding than to get half with the program and realize you're miserable. 97 00:04:06,000 --> 00:04:09,000 And from the perspective of workforce agencies, it's huge. 98 00:04:09,000 --> 00:04:18,000 They can allocate their funding more effectively, making sure that resources are going to people who are likely to complete the training and actually enter the tech workforce. 99 00:04:18,000 --> 00:04:20,000 It's a win-win, really. 100 00:04:20,000 --> 00:04:25,000 Learners get a better sense of their own potential, and agencies can maximize their impact. 101 00:04:25,000 --> 00:04:27,000 But let's talk about the bigger picture here. 102 00:04:27,000 --> 00:04:32,000 Why is this approach so effective compared to traditional admissions methods? 103 00:04:32,000 --> 00:04:35,000 Well, as we've been saying, it's all about preventing dropouts. 104 00:04:35,000 --> 00:04:39,000 Traditional methods focus on academic performance, but that doesn't tell you the whole story. 105 00:04:39,000 --> 00:04:40,000 Right. 106 00:04:40,000 --> 00:04:48,000 Someone might have stellar grades, but lack the motivation or the personality traits that are crucial for success in a fast-paced tech environment. 107 00:04:48,000 --> 00:04:49,000 Exactly. 108 00:04:49,000 --> 00:04:56,000 And by taking a more holistic approach, automation work is able to identify those factors early on and guide people towards the right path. 109 00:04:56,000 --> 00:04:59,000 It's like they're setting you up for success from day one. 110 00:04:59,000 --> 00:05:04,000 And that's huge, not just for the individual, but for society as a whole. 111 00:05:04,000 --> 00:05:05,000 Absolutely. 112 00:05:05,000 --> 00:05:12,000 We need skilled tech professionals, and this approach ensures that people are getting the training they need and actually finishing those programs. 113 00:05:12,000 --> 00:05:14,000 That's all connected, right? 114 00:05:14,000 --> 00:05:17,000 Individual success, workforce development, the strength of our economy. 115 00:05:17,000 --> 00:05:18,000 Yeah. 116 00:05:18,000 --> 00:05:20,000 It all ties back to education and training. 117 00:05:20,000 --> 00:05:21,000 Couldn't agree more. 118 00:05:21,000 --> 00:05:25,000 And it makes you think, could this type of approach be applied to other fields? 119 00:05:25,000 --> 00:05:31,000 Like imagine if we could predict success with this level of accuracy in health care or education. 120 00:05:31,000 --> 00:05:32,000 Right. 121 00:05:32,000 --> 00:05:33,000 We need to collect. 122 00:05:33,000 --> 00:05:37,000 How could we adapt this personalized data-driven model to different learning journeys? 123 00:05:37,000 --> 00:05:39,000 It's a fascinating question. 124 00:05:39,000 --> 00:05:44,000 And one that deserves further exploration, we might not have all the answers yet, but it's definitely something to ponder. 125 00:05:44,000 --> 00:05:45,000 Absolutely. 126 00:05:45,000 --> 00:06:07,000 And make you wonder what the future of education and training might look like if we embrace this kind of innovation.