1 00:00:00,870 --> 00:00:05,031 Welcome to SLP nerd cast your favorite professional resource for evidence based 2 00:00:05,031 --> 00:00:06,981 practice in speech, language pathology. 3 00:00:07,341 --> 00:00:09,591 I'm Kate grant wa and I'm Amy 4 00:00:09,591 --> 00:00:10,071 Wonka. 5 00:00:10,371 --> 00:00:13,131 We are both speech, language pathologists working in the field 6 00:00:13,131 --> 00:00:15,381 and co-founders of SLP nerd cast. 7 00:00:15,681 --> 00:00:15,981 Each 8 00:00:15,981 --> 00:00:19,471 episode of this podcast is a course offered for ashes EU. 9 00:00:20,051 --> 00:00:23,181 Our podcast audio courses are here to help you level up your 10 00:00:23,181 --> 00:00:26,121 knowledge and earn those professional development hours that you need. 11 00:00:26,871 --> 00:00:27,741 This course. 12 00:00:27,771 --> 00:00:33,201 Plus the corresponding short post test is equal to one certificate of attendance to 13 00:00:33,201 --> 00:00:35,811 earn CEUs today and take the post test. 14 00:00:35,811 --> 00:00:38,721 After this session, follow the link provided in the show 15 00:00:38,721 --> 00:00:41,001 notes or head to SLP ncast.com. 16 00:00:41,751 --> 00:00:45,291 Before we get started one quick, disclaimer, our courses are 17 00:00:45,291 --> 00:00:46,731 not meant to replace clinical. 18 00:00:47,391 --> 00:00:52,071 We do not endorse products, procedures, or other services mentioned by 19 00:00:52,071 --> 00:00:53,571 our guests, unless otherwise 20 00:00:53,571 --> 00:00:54,201 specified. 21 00:00:54,711 --> 00:00:55,641 We hope you enjoy 22 00:00:55,641 --> 00:00:56,181 the course. 23 00:00:58,371 --> 00:01:00,261 Are you an SLP related professional? 24 00:01:00,681 --> 00:01:04,191 The SLP nerd cast unlimited subscription gives members access to 25 00:01:04,191 --> 00:01:08,901 over 100 courses, offered for ashes, EU, and certificates of attendance. 26 00:01:09,531 --> 00:01:14,541 With SLP nerd cast membership, you can earn unlimited EU all year at any time. 27 00:01:15,681 --> 00:01:19,371 SLP nerd cast courses are unique evidence based with a focus 28 00:01:19,376 --> 00:01:20,721 on information that is useful. 29 00:01:21,591 --> 00:01:25,461 When you join SLP nerd cast as a member, you'll have access to the best online 30 00:01:25,461 --> 00:01:28,581 platform for continuing education and speech and language pathology. 31 00:01:29,781 --> 00:01:34,131 Join as a member today and save 10% using code nerd caster 10. 32 00:01:34,821 --> 00:01:36,711 A link for membership is in the show notes 33 00:01:44,915 --> 00:01:46,985 Welcome everyone to SLP Nerdcast. 34 00:01:47,005 --> 00:01:51,845 We are really excited for another edition of SLP On Demand. 35 00:01:52,185 --> 00:01:56,685 For those of you listening for the first time, SLP On Demand is a series 36 00:01:56,685 --> 00:02:02,275 that we put out occasionally where we answer questions from our audience. 37 00:02:02,275 --> 00:02:05,655 So if you are a member and you have a clinical question you can write 38 00:02:05,655 --> 00:02:09,615 in and our doctor of speech language pathology who is here with us Dr. 39 00:02:09,615 --> 00:02:12,905 Ana Paula Moomy will answer your question. 40 00:02:12,965 --> 00:02:14,665 Welcome Ana Paula. 41 00:02:15,255 --> 00:02:16,005 Thank you. 42 00:02:16,845 --> 00:02:18,595 I'm very excited for today's question. 43 00:02:18,615 --> 00:02:21,265 It touches something that I do for a living. 44 00:02:21,595 --> 00:02:24,945 I was really excited to kind of catch up with you before we hit the 45 00:02:24,945 --> 00:02:28,105 record button and learn a little bit more about what the research says, 46 00:02:28,415 --> 00:02:29,765 because that's always fun for me. 47 00:02:30,085 --> 00:02:34,455 Before we read our clinical question aloud, I am going to 48 00:02:34,495 --> 00:02:38,155 quickly review our learning objectives for today's discussion. 49 00:02:38,665 --> 00:02:42,805 Learning objective number one, identify the relationship between data collection, 50 00:02:42,845 --> 00:02:44,555 target selection, and goal writing. 51 00:02:45,050 --> 00:02:48,380 And learning objective number two, identify at least two different 52 00:02:48,390 --> 00:02:52,270 types of data collection that can be used when working with AAC users. 53 00:02:54,815 --> 00:02:59,165 Uh, anyone who is listening can also find information about, uh, 54 00:02:59,205 --> 00:03:02,585 our financial and non financial disclosures in the show notes. 55 00:03:03,135 --> 00:03:07,865 And Apollo, why don't you get us started by reading aloud our listener's question? 56 00:03:08,805 --> 00:03:09,345 Sure. 57 00:03:09,735 --> 00:03:14,465 So the question relates to resources for data collection for AAC users. 58 00:03:14,565 --> 00:03:16,945 And Andy, um, who wrote in. 59 00:03:17,305 --> 00:03:22,235 Um, stated that her mentee has 14 life skills elementary students on her 60 00:03:22,235 --> 00:03:26,645 caseload, and she's looking for ways to help her efficiently track device 61 00:03:26,645 --> 00:03:28,755 use throughout her student's day. 62 00:03:30,145 --> 00:03:30,675 So that's a 63 00:03:30,735 --> 00:03:32,035 really big question. 64 00:03:32,625 --> 00:03:33,085 Yes. 65 00:03:34,185 --> 00:03:36,665 And I think we just have to first acknowledge, like, this is a big 66 00:03:36,665 --> 00:03:40,675 question that's hard to answer, um, because there is so much that we don't 67 00:03:40,685 --> 00:03:42,595 know about these particular students. 68 00:03:42,625 --> 00:03:44,485 And there's also. 69 00:03:44,725 --> 00:03:47,815 Um, we don't know specifics about what devices they're using. 70 00:03:47,935 --> 00:03:48,955 What does that look like? 71 00:03:48,955 --> 00:03:52,425 And so, um, I would say just in general, this is gonna be a little bit 72 00:03:52,435 --> 00:03:57,865 difficult to, to touch on, but, um, also acknowledging that I would say data 73 00:03:57,865 --> 00:04:00,115 collection is tricky regardless, right? 74 00:04:00,135 --> 00:04:05,475 It's strictly tricky whether, um, we're working on articulation or language 75 00:04:05,495 --> 00:04:06,965 or it doesn't really matter, right? 76 00:04:07,005 --> 00:04:07,985 All of these areas. 77 00:04:08,295 --> 00:04:09,785 Um, especially. 78 00:04:10,600 --> 00:04:17,400 Tracking, um, data or taking data without sacrificing genuine engagement with the 79 00:04:17,400 --> 00:04:18,710 person that's in front of you, right? 80 00:04:18,710 --> 00:04:20,570 So that's the big thing. 81 00:04:20,570 --> 00:04:24,250 I think, um, I work a lot with grad students and I think about how 82 00:04:24,250 --> 00:04:29,060 sometimes they're so attuned to the data collection process that they 83 00:04:29,060 --> 00:04:31,950 forget, like, oh, wait, but there's a person in front of me and I should 84 00:04:31,960 --> 00:04:35,370 be engaging and just really building that relationship and the rapport. 85 00:04:35,370 --> 00:04:35,805 So, yeah. 86 00:04:36,525 --> 00:04:40,815 Um, I just wanted to acknowledge those, uh, setbacks in a sense, 87 00:04:40,815 --> 00:04:43,375 right, um, related to data collection. 88 00:04:43,985 --> 00:04:48,415 And to kind of piggyback on that, obviously this episode is likely, 89 00:04:48,485 --> 00:04:50,035 you know, it's under an hour long. 90 00:04:50,515 --> 00:04:54,745 Uh, we are not going to be able to cover everything about data collection 91 00:04:54,765 --> 00:04:56,855 in this short amount of time. 92 00:04:57,335 --> 00:05:02,505 Uh, anyone who is listening who would like to learn more about data collection, 93 00:05:02,635 --> 00:05:06,205 either while you're listening to this episode or after this episode is over. 94 00:05:06,620 --> 00:05:12,775 We do have three or maybe even four episodes specifically on Monitoring 95 00:05:12,775 --> 00:05:17,435 progress and data collection, including probe data or discontinuous 96 00:05:17,495 --> 00:05:19,925 data, which I know we're going to talk a little bit about today, 97 00:05:19,995 --> 00:05:21,825 uh, that is a very complex topic. 98 00:05:21,825 --> 00:05:25,005 So if you are listening and you know already that that's something 99 00:05:25,005 --> 00:05:27,725 that you'd want to learn more about, check out the show notes. 100 00:05:27,725 --> 00:05:31,315 We will link, um, we will put links to all of those episodes in the show notes for 101 00:05:32,965 --> 00:05:33,985 you. 102 00:05:34,585 --> 00:05:39,705 So I wanted to focus on one article in just my research and really 103 00:05:39,705 --> 00:05:40,955 just admitting, first of all, that. 104 00:05:41,170 --> 00:05:43,510 I am not an expert on AAC. 105 00:05:43,530 --> 00:05:46,710 And so it's an area that is a stretch for me. 106 00:05:46,710 --> 00:05:51,460 And so, um, as I looked through some of the research, um, I found one helpful 107 00:05:51,480 --> 00:05:55,450 article, um, on data collection and monitoring AAC intervention in the 108 00:05:55,450 --> 00:06:02,450 schools, um, by Katya Hill, um, in 2009 in the perspectives on AAC, a journal. 109 00:06:02,470 --> 00:06:09,115 And I appreciated how they talked about, um, collecting the data, To 110 00:06:09,115 --> 00:06:12,835 collect, depending on the design and the targets of the intervention program. 111 00:06:13,015 --> 00:06:15,475 So, in other words, really thinking about, like, what are we actually 112 00:06:15,485 --> 00:06:17,745 tracking in relation to device use? 113 00:06:18,095 --> 00:06:24,525 And, um, they divide up 2, uh, areas or talk through 2 different areas. 114 00:06:24,990 --> 00:06:27,330 Um, performance data and outcomes data. 115 00:06:27,330 --> 00:06:31,880 So with performance data, um, representing really the quantification 116 00:06:31,940 --> 00:06:34,100 of specific language targets. 117 00:06:34,110 --> 00:06:40,690 So things like, uh, spontaneous or novel utterances, um, communication rate 118 00:06:40,740 --> 00:06:46,420 potentially, or any word based measures, um, that might include things like. 119 00:06:46,800 --> 00:06:51,820 Uh, total number of words used, or maybe it's percentage of core vocabulary 120 00:06:51,820 --> 00:06:57,730 that's used, um, or mean length of utterance, um, even diversity of words. 121 00:06:57,740 --> 00:07:01,890 So is there, um, a mixture, right, of are they using nouns, 122 00:07:01,890 --> 00:07:03,250 verbs, adjectives, and so on? 123 00:07:03,250 --> 00:07:07,640 So just looking at these word based measures and other types 124 00:07:07,640 --> 00:07:10,150 of performance, um, data. 125 00:07:10,660 --> 00:07:15,470 And then the other Area was outcomes data really representing the results 126 00:07:15,470 --> 00:07:20,200 of intervention that's related to things like quality of life and 127 00:07:20,220 --> 00:07:22,390 satisfaction and functionality. 128 00:07:22,390 --> 00:07:27,780 So, um, that was helpful to me to categorize, um, and make that distinction. 129 00:07:28,110 --> 00:07:31,180 Um, and because our goal. 130 00:07:31,690 --> 00:07:36,350 And this is what they talk through is to optimize communication in 131 00:07:36,350 --> 00:07:38,080 a student's daily environment. 132 00:07:38,500 --> 00:07:42,370 Um, then we really should have both performance data that's collected in 133 00:07:42,370 --> 00:07:47,160 those environments and then also outcome measures, um, that report, you know, 134 00:07:47,160 --> 00:07:52,940 perceptions or satisfaction of performance by, um, Those closest to the student. 135 00:07:52,940 --> 00:07:55,890 So that could be teachers, of course, caregivers. 136 00:07:56,150 --> 00:07:59,160 Um, but then, of course, the student him or herself, right? 137 00:07:59,210 --> 00:08:06,500 And so I did wonder, Kate, if you would just touch on, um, examples of those, 138 00:08:06,540 --> 00:08:11,380 the performance data that might be tied to or, or more appropriate for complex 139 00:08:11,480 --> 00:08:18,555 learners, because this might be easier for a child who, um, Um, is maybe more verbal, 140 00:08:18,855 --> 00:08:24,805 but not one that is, um, where, where there's just more complex, um, profiles. 141 00:08:24,805 --> 00:08:26,875 So if you wanted to talk about that for a little bit, I 142 00:08:26,875 --> 00:08:28,195 would love to hear your input. 143 00:08:28,595 --> 00:08:28,955 Sure. 144 00:08:28,985 --> 00:08:32,755 So, I mean, anyone who's listening to this podcast knows that, or has been listening 145 00:08:32,755 --> 00:08:34,155 for a while, that this is my jam. 146 00:08:34,280 --> 00:08:37,060 This is the, this is my clinical wheelhouse. 147 00:08:37,080 --> 00:08:40,950 I'm very fortunate to have worked in AAC for the last almost 20 years now. 148 00:08:41,390 --> 00:08:44,330 Uh, not quite 20 years, but over 15, not that we're counting. 149 00:08:44,730 --> 00:08:48,830 Um, and this particular profile, complex learners, emergent 150 00:08:48,840 --> 00:08:52,620 learners, early language learners, is what I, what I love to do. 151 00:08:53,220 --> 00:08:56,770 Um, I really appreciate the way that you've described, at least from this 152 00:08:56,780 --> 00:09:00,465 article, these two different categories of data collection, um, that you've 153 00:09:00,565 --> 00:09:05,305 Because I think often when people think of data collection, they think of tally 154 00:09:05,305 --> 00:09:07,485 notes scribbled on a sticky, right? 155 00:09:07,525 --> 00:09:11,425 You know, we're going back to your point of not wanting to sacrifice connection. 156 00:09:11,790 --> 00:09:15,560 We grab what we have, and we, oh gosh, I've got this, I've got this goal 157 00:09:15,590 --> 00:09:20,130 on whatever it is, and so we, we, we scribble our tally notes, and we 158 00:09:20,130 --> 00:09:22,790 think that that's our data collection, and yes, that is data collection. 159 00:09:23,180 --> 00:09:25,700 Uh, is it quality data collection? 160 00:09:25,760 --> 00:09:26,770 Perhaps not. 161 00:09:27,180 --> 00:09:31,955 Um, and I, I really just wanted to take a second to, um, to think to 162 00:09:32,155 --> 00:09:36,945 at least appreciate the different qualifiers when it comes to the kind 163 00:09:36,975 --> 00:09:38,905 of data that you are collecting. 164 00:09:38,915 --> 00:09:44,125 That is a really important first thought to kind of zoom back to this 165 00:09:44,145 --> 00:09:48,750 learner's question or this member's question about what recommendations 166 00:09:48,750 --> 00:09:49,680 they can make to their mentor. 167 00:09:49,680 --> 00:09:52,700 And I think the first recommendation based on that article from what I'm hearing from 168 00:09:52,700 --> 00:09:55,680 you is really reflecting on your purpose. 169 00:09:55,720 --> 00:09:58,100 What are you taking data about? 170 00:09:58,570 --> 00:09:59,890 Is it outcomes related? 171 00:09:59,900 --> 00:10:01,450 Is it performance related? 172 00:10:01,510 --> 00:10:06,460 Is it aligned with our EBP model in terms of considering clinician's perspective, 173 00:10:06,500 --> 00:10:08,240 client's perspectives and values? 174 00:10:08,690 --> 00:10:13,180 Um, I think when you keep that as a lens, it's a lot easier to 175 00:10:13,180 --> 00:10:15,100 then zoom in a little bit further. 176 00:10:15,100 --> 00:10:21,645 Um, and think about what data collection methods are most appropriate, uh, to your 177 00:10:21,645 --> 00:10:24,645 next step or to the targets that you're, that you're trying to work towards. 178 00:10:24,645 --> 00:10:27,075 Now, I know that I just went really off topic, but to answer your 179 00:10:27,075 --> 00:10:30,855 question about an example for a more, a complex learner or an emergent 180 00:10:30,865 --> 00:10:36,245 learner, um, I think that when you're, first of all, every child is unique. 181 00:10:36,345 --> 00:10:37,425 Every learner is unique. 182 00:10:37,435 --> 00:10:41,835 There is no, I have, I have big feelings when I hear things like, well, this is the 183 00:10:41,835 --> 00:10:48,135 way it's done or this is what we do here or no, you are always customizing your AAC 184 00:10:48,145 --> 00:10:53,945 intervention to your learner, especially if that learner has a complex profile. 185 00:10:53,955 --> 00:10:59,295 So a number one, um, you're always making data driven decisions, person driven 186 00:10:59,295 --> 00:11:01,385 decisions, patient centered decisions. 187 00:11:01,725 --> 00:11:04,405 Um, particularly when there is complexity involved. 188 00:11:05,295 --> 00:11:08,575 And a lot of com, when you're working with complex learners, often your 189 00:11:08,585 --> 00:11:13,805 first objectives are related to teaching symbolic exchange, teaching 190 00:11:13,805 --> 00:11:16,695 the use of symbolic, uh, language. 191 00:11:16,745 --> 00:11:20,085 Now, when I say symbolic exchange, I'm not talking about pecs before anybody 192 00:11:20,085 --> 00:11:23,555 gets a little grouchy thinking about pecs and all of the grouchy feelings 193 00:11:23,565 --> 00:11:25,155 that we've developed about pecs. 194 00:11:25,435 --> 00:11:30,760 We are talking about moving through a developmental lens to teach a person 195 00:11:30,770 --> 00:11:33,030 how to use symbols to communicate. 196 00:11:33,460 --> 00:11:38,970 Um, and I think, you know, that can look like a lot of different things that can, 197 00:11:38,980 --> 00:11:43,640 when you're talking about AAC, depending on your learner, that could look like 198 00:11:44,360 --> 00:11:46,780 point selecting icons in a sequence. 199 00:11:47,090 --> 00:11:50,650 It could look like scanning a visual field to select an icon 200 00:11:50,650 --> 00:11:52,485 and make a purposeful choice. 201 00:11:53,320 --> 00:11:56,260 It could look like sequencing two icons. 202 00:11:56,500 --> 00:12:03,900 together to produce voice output, uh, it could, it could be producing one symbol 203 00:12:03,910 --> 00:12:09,520 for a function that isn't just requesting or perhaps they are an emergent learner 204 00:12:09,520 --> 00:12:13,240 and they're, you know, in developmentally making requests and making basic 205 00:12:13,250 --> 00:12:15,360 wants and needs known is a main goal. 206 00:12:15,690 --> 00:12:19,960 So you want them to produce a single symbol to get their 207 00:12:19,960 --> 00:12:22,580 wants and needs met and then everybody's throwing a party, right? 208 00:12:22,580 --> 00:12:30,330 So it really will depend, um, So much on who the learner is in terms of choosing 209 00:12:30,330 --> 00:12:34,340 that those targets and choosing that data collection strategy for performance. 210 00:12:34,860 --> 00:12:38,940 If anyone is listening wants to learn more about, um, the lens 211 00:12:38,960 --> 00:12:43,090 of AAC and going back to basics, we did a great interview with Dr. 212 00:12:43,090 --> 00:12:44,870 Kathy Binger, um, and Dr. 213 00:12:44,870 --> 00:12:49,020 Ken, Jennifer Kent Walsh called AAC back to basics that really 214 00:12:49,020 --> 00:12:51,730 specifically takes a good look at what. 215 00:12:52,160 --> 00:12:56,950 Um, the intersection of AAC and language development and how we can 216 00:12:56,960 --> 00:12:58,510 better integrate those two things. 217 00:12:58,510 --> 00:12:59,580 Did I answer your question? 218 00:12:59,580 --> 00:13:01,220 I know I went on like four tangents. 219 00:13:01,620 --> 00:13:02,510 No, you did. 220 00:13:02,510 --> 00:13:02,680 And 221 00:13:02,910 --> 00:13:03,110 thank you. 222 00:13:03,120 --> 00:13:03,400 That's 223 00:13:03,410 --> 00:13:04,040 perfect. 224 00:13:05,450 --> 00:13:06,230 I appreciate it. 225 00:13:06,280 --> 00:13:12,415 Um, that makes it, uh, a lot clearer and, um, for sure, Just having those 226 00:13:12,415 --> 00:13:14,275 tangible examples are super helpful. 227 00:13:14,815 --> 00:13:19,445 Um, another recommendation that I found in the initial stages of device use, 228 00:13:19,485 --> 00:13:22,985 which kind of goes back to a little bit of what you were saying, um, was 229 00:13:22,985 --> 00:13:27,905 to actually take data on what the SLP or the communication partner is doing. 230 00:13:28,175 --> 00:13:32,510 So, um, there were some really helpful questions, um, Again, for 231 00:13:32,520 --> 00:13:35,540 me, because this is not my area, um, that helped me think through 232 00:13:35,540 --> 00:13:36,770 like, okay, so what does that mean? 233 00:13:36,770 --> 00:13:37,410 Exactly. 234 00:13:37,610 --> 00:13:41,890 So things like how often does the student have access to 235 00:13:41,890 --> 00:13:43,250 their system throughout the day? 236 00:13:44,120 --> 00:13:46,860 That is a pretty important question, right? 237 00:13:46,880 --> 00:13:51,350 And then how many opportunities did the student have to actually use their device? 238 00:13:52,100 --> 00:13:56,240 Um, another question, how often are adults modeling on the device? 239 00:13:56,270 --> 00:13:59,980 And so that modeling component being huge, and maybe you could 240 00:13:59,980 --> 00:14:01,330 speak to that a little bit more. 241 00:14:01,670 --> 00:14:02,070 I was going to 242 00:14:02,070 --> 00:14:04,130 say, I've got a great example for that, but keep going. 243 00:14:04,130 --> 00:14:06,470 Yes, so I have one more here. 244 00:14:06,680 --> 00:14:11,090 Um, how often is the student attending to the modeling that's provided? 245 00:14:11,390 --> 00:14:16,500 So again, this isn't necessarily looking at the output from the child. 246 00:14:16,660 --> 00:14:19,040 It's really more talking about the input, right? 247 00:14:19,040 --> 00:14:20,050 What is happening? 248 00:14:20,370 --> 00:14:24,700 Um, with the individuals around that child who are doing 249 00:14:24,700 --> 00:14:28,640 something or providing access or providing that modeling and so on. 250 00:14:28,640 --> 00:14:31,260 So yes, please give me examples. 251 00:14:31,310 --> 00:14:33,300 I was going to say, I was like jumping in my seat because I have 252 00:14:33,300 --> 00:14:34,280 such a great example for this. 253 00:14:34,290 --> 00:14:36,590 So backing up really quickly. 254 00:14:37,115 --> 00:14:40,005 Back to our sticky note with tally marks on it, right? 255 00:14:40,315 --> 00:14:42,265 We think that that is frequency data. 256 00:14:42,385 --> 00:14:45,375 So frequency data collection strategies would be, you know, 257 00:14:45,375 --> 00:14:48,325 marking every single instance of the target behavior that happened. 258 00:14:48,345 --> 00:14:50,525 And again, we're not going to get into this in detail. 259 00:14:50,635 --> 00:14:54,065 We will list additional references or episodes in the show notes. 260 00:14:55,365 --> 00:14:58,545 Unpacks a lot more of different kinds of strategies of data collection. 261 00:14:58,555 --> 00:14:59,595 Frequency is one of them. 262 00:15:00,045 --> 00:15:02,235 Percentage, who doesn't love a good percentage? 263 00:15:02,235 --> 00:15:05,715 I think we over rely on them out of 80 percent of opportunities, right? 264 00:15:05,715 --> 00:15:10,435 Everybody knows how to take percentage data Um and rates how many times you 265 00:15:10,435 --> 00:15:14,230 do things in a certain period of time Those are a pretty common data collection 266 00:15:14,230 --> 00:15:15,710 strategies in speech pathology. 267 00:15:15,960 --> 00:15:19,040 One of the less common ones that I love, and I swear I'm going to answer 268 00:15:19,040 --> 00:15:21,310 your question, is trials to criterion. 269 00:15:21,950 --> 00:15:27,000 Trials to criterion is a data collection strategy where you're looking at the 270 00:15:27,000 --> 00:15:31,950 number of opportunities or the number of trials, trials to criterion, that a 271 00:15:32,160 --> 00:15:35,210 person needs to achieve a certain outcome. 272 00:15:35,510 --> 00:15:39,300 Threshold or to achieve a predetermined set of mastery. 273 00:15:40,160 --> 00:15:45,770 The reason that I love trials to criterion is because I have applied it to measuring 274 00:15:45,820 --> 00:15:48,220 the behavior of communication partners. 275 00:15:48,350 --> 00:15:49,280 And this is my story. 276 00:15:49,350 --> 00:15:51,885 I consult to a wide variety of. 277 00:15:52,045 --> 00:15:55,095 of programs in in Massachusetts area. 278 00:15:55,625 --> 00:15:58,155 Uh, because I'm a BCBA, don't anybody hate me. 279 00:15:58,155 --> 00:15:58,835 I'm not evil. 280 00:15:59,225 --> 00:16:02,015 Because I'm a BCBA, I work a lot with behavioral programs. 281 00:16:02,015 --> 00:16:07,205 I work a lot with BCBAs, um, and trying to integrate some of this speech pathology. 282 00:16:07,395 --> 00:16:11,795 research, knowledge, best practice, person centered care in some of these programs. 283 00:16:12,565 --> 00:16:19,235 And in that work, we have one, one program in particular, we did a lot 284 00:16:19,275 --> 00:16:24,075 of patient education, a lot of teacher education around the importance of 285 00:16:24,075 --> 00:16:27,915 modeling, around the importance of language bombardment, around the 286 00:16:27,915 --> 00:16:34,505 importance of making a, making someone's, um, program linguistically rich. 287 00:16:34,905 --> 00:16:40,685 And what we did was To kind of flip the script, we said, okay, how many 288 00:16:40,685 --> 00:16:45,345 trials does this one particular complex learner need to produce a word? 289 00:16:45,355 --> 00:16:46,945 How many models do they need? 290 00:16:47,795 --> 00:16:51,905 And what's nice about this is that we switch from asking the question, what 291 00:16:51,905 --> 00:16:55,045 does a student know, to how do they learn? 292 00:16:55,530 --> 00:16:59,220 Once you know how a complex student learns, rinse, repeat, you've got the 293 00:16:59,220 --> 00:17:02,260 recipe, let's make all the cookies, let's make all the words, let's, let's 294 00:17:02,260 --> 00:17:06,460 do this again and again and again, but when you really flip your thinking to 295 00:17:06,460 --> 00:17:12,140 thinking about asking questions and taking data to learn about how they learn 296 00:17:12,190 --> 00:17:18,130 instead of what they know, you can really apply that to the entire environment. 297 00:17:18,140 --> 00:17:23,260 So in this particular example, we took trials to criterion data on the number 298 00:17:23,260 --> 00:17:24,940 of models that were provided in a day. 299 00:17:26,005 --> 00:17:30,705 And to learn how many times did this one kiddo need to get exposed to this one 300 00:17:30,705 --> 00:17:32,815 word for them to be able to produce it. 301 00:17:32,815 --> 00:17:34,045 And the answer was hundreds. 302 00:17:34,875 --> 00:17:38,525 What's amazing is that he was able to get hundreds of exposures in a 303 00:17:38,525 --> 00:17:41,835 short period of time because the staff got super competitive and they, 304 00:17:42,155 --> 00:17:46,255 and you know, they started becoming more aware of their own behavior and 305 00:17:46,255 --> 00:17:51,195 their own roles and responsibilities as communication partners. 306 00:17:51,565 --> 00:17:52,415 Um, so. 307 00:17:52,510 --> 00:17:56,400 So another tangent, I guess that's my, my function and my role here today is 308 00:17:56,400 --> 00:18:00,030 to go off on these tangents, but it's the different data collection strategies 309 00:18:00,040 --> 00:18:03,760 you choose can really help you shift the way you're thinking about where 310 00:18:03,760 --> 00:18:08,310 that, where the quality data comes from because it's not just your communication. 311 00:18:08,420 --> 00:18:10,150 It's not just, it's not just your student. 312 00:18:10,440 --> 00:18:12,030 It's not just your client. 313 00:18:12,460 --> 00:18:14,510 You could be looking at data about the environment. 314 00:18:14,520 --> 00:18:16,780 You could be looking at data about the communication partners. 315 00:18:16,790 --> 00:18:18,280 We're really talking about a whole. 316 00:18:18,470 --> 00:18:22,270 a whole human and a whole microcosm, a whole environment, 317 00:18:22,280 --> 00:18:27,570 a whole set of variables that we need to consider for AAC success. 318 00:18:28,300 --> 00:18:29,740 I hope I answered your question again. 319 00:18:30,330 --> 00:18:30,880 Yes. 320 00:18:31,040 --> 00:18:31,590 Thank you. 321 00:18:34,030 --> 00:18:36,870 One more thing that I wanted to touch on before I'm going to pose another 322 00:18:36,870 --> 00:18:42,080 question to you, Kate, is I found just a variety of data collection sheets 323 00:18:42,100 --> 00:18:43,740 that were downloadable for free. 324 00:18:44,050 --> 00:18:47,220 And again, for me, it was helpful just to think through, like, how are 325 00:18:47,240 --> 00:18:50,130 they structured and, you know, just in different ways and organized. 326 00:18:50,130 --> 00:18:54,860 And so there were some that were goal based versus prompt based data collection. 327 00:18:54,860 --> 00:18:58,120 And so one example was, um, data collection that was 328 00:18:58,120 --> 00:19:00,710 based on, um, modeled words. 329 00:19:00,770 --> 00:19:06,010 So you select a word and then show, of course, the child, you know, what 330 00:19:06,020 --> 00:19:07,280 happens when you select that word. 331 00:19:07,280 --> 00:19:11,640 And so having that, um, sequence of modeling and then seeing, 332 00:19:11,720 --> 00:19:15,765 um, Or giving them a taste of what does that produce, right? 333 00:19:15,795 --> 00:19:19,125 Or what's the outcome after that happens? 334 00:19:19,455 --> 00:19:25,910 Um, and then, uh, the, Um, it was also, uh, a word selected by the child 335 00:19:25,920 --> 00:19:30,910 after a prompt was provided, um, and then a word selected spontaneously 336 00:19:30,920 --> 00:19:32,750 by the child without any prompting. 337 00:19:32,750 --> 00:19:38,390 So they had essentially like an MPS format where you were tracking modeled 338 00:19:38,390 --> 00:19:42,810 words, words that were prompted, and then words that were spontaneous. 339 00:19:42,810 --> 00:19:47,100 So MPS, um, was one way that it was structured. 340 00:19:47,370 --> 00:19:52,765 Um, another one, um, another example was, um, Based on a variety of language 341 00:19:52,765 --> 00:20:01,275 functions like requesting protesting are they commenting describing negotiating 342 00:20:01,275 --> 00:20:06,195 and so on so there was lots of different options to think through because I, um. 343 00:20:07,070 --> 00:20:11,400 I feel like so often we get stuck on just requesting, right? 344 00:20:11,410 --> 00:20:12,870 It's just a request a button. 345 00:20:12,880 --> 00:20:17,160 It's just and that's like the only thing that counts or that really 346 00:20:17,160 --> 00:20:20,540 is being monitored when there's so much more right that we can 347 00:20:20,540 --> 00:20:26,470 look for when it comes to language usage beyond just number of words. 348 00:20:26,590 --> 00:20:27,070 So. 349 00:20:27,730 --> 00:20:31,610 Those were really helpful for me to look through, just in terms of thinking 350 00:20:31,610 --> 00:20:35,930 about, you know, efficiently tracking usage, um, with different parameters. 351 00:20:36,110 --> 00:20:39,660 Do you have anything to add, um, in relation to, to that? 352 00:20:40,310 --> 00:20:40,710 I, I 353 00:20:40,740 --> 00:20:41,210 think, 354 00:20:41,270 --> 00:20:43,500 you know, everybody wants a good data sheet. 355 00:20:43,750 --> 00:20:47,210 You know, data sheets are better than your sticky note with 356 00:20:47,210 --> 00:20:48,630 Scratch with, with tally marks. 357 00:20:48,650 --> 00:20:51,910 I think something that you bring up that's really important to think 358 00:20:51,910 --> 00:20:56,370 about is the relationship between data collection and goal writing. 359 00:20:56,540 --> 00:21:01,680 Uh, we, again, this is a whole other, this is a whole episode that we can 360 00:21:01,680 --> 00:21:05,220 link that we've done on the importance of measurement target selection. 361 00:21:05,700 --> 00:21:07,600 Uh, we will link that in the show notes as well. 362 00:21:07,910 --> 00:21:12,820 Um, the short CliffsNotes version is We think of data collection and goal writing 363 00:21:12,820 --> 00:21:14,160 as something that happens in a sequence. 364 00:21:14,160 --> 00:21:16,490 So first we write our goal, then we take our data. 365 00:21:16,580 --> 00:21:18,250 And that's absolutely not the case. 366 00:21:18,610 --> 00:21:21,920 We need to be thinking that these are two things that happen in tandem. 367 00:21:22,270 --> 00:21:23,740 They influence one another. 368 00:21:23,940 --> 00:21:26,490 Uh, we want to be thinking, before we write our goal, we want to be 369 00:21:26,490 --> 00:21:29,510 thinking about what kind of, what's the data collection going to look like? 370 00:21:29,550 --> 00:21:30,420 Is it reasonable? 371 00:21:30,430 --> 00:21:31,230 Is it doable? 372 00:21:31,290 --> 00:21:32,570 Who's collecting the data? 373 00:21:32,690 --> 00:21:34,530 How often is it going to get collected? 374 00:21:34,970 --> 00:21:38,410 Um, you also want to think about your target when you're writing your goal and 375 00:21:38,410 --> 00:21:39,920 how your target's going to get measured. 376 00:21:39,940 --> 00:21:43,360 Is it a target skill that's really fleeting and you have 377 00:21:43,360 --> 00:21:45,540 to be watching the entire time? 378 00:21:45,570 --> 00:21:48,050 Is it a target skill that is prolonged? 379 00:21:48,510 --> 00:21:50,730 Um, is it something that's low frequency? 380 00:21:50,730 --> 00:21:54,250 So you're going to be lucky if it happens once a day, or is it high 381 00:21:54,250 --> 00:21:57,880 frequency where it's potentially happening multiple times in a half hour? 382 00:21:58,320 --> 00:22:03,800 Um, all of these things are really important to consider when you are 383 00:22:03,800 --> 00:22:05,670 thinking about your data collection. 384 00:22:06,140 --> 00:22:13,035 Uh, and when it comes to recording your data, There are a lot of 385 00:22:13,035 --> 00:22:15,325 different ways in AAC to do that. 386 00:22:15,325 --> 00:22:19,365 I think the way that the best way is the one that works for you, that 387 00:22:19,365 --> 00:22:23,265 keeps your hands free, that keeps your attention on your client. 388 00:22:23,615 --> 00:22:29,655 Um, there are two strategies that I think are, um, there are a handful 389 00:22:29,655 --> 00:22:33,235 that I think could be considered that I think are worth considering. 390 00:22:33,925 --> 00:22:36,975 The first is maybe a tally counter or a golf counter. 391 00:22:36,975 --> 00:22:38,965 I don't know why it's called a golf counter, but you know, 392 00:22:38,965 --> 00:22:40,505 they're like little clicker. 393 00:22:40,755 --> 00:22:41,845 They're like bouncers. 394 00:22:41,845 --> 00:22:45,815 You see them at like the, at the clubs, uh, you know, uh, clicking 395 00:22:45,925 --> 00:22:50,005 for, for capacity in a, in a, you know, in a bar or whatever. 396 00:22:50,435 --> 00:22:54,695 Um, those are nice to kind of hang on your belt with a carabiner. 397 00:22:54,735 --> 00:22:56,345 You could do one on each side. 398 00:22:56,345 --> 00:22:59,640 And then, uh, Um, if you're doing percentage data, one, your right 399 00:22:59,640 --> 00:23:01,390 side is for successful trials. 400 00:23:01,390 --> 00:23:04,170 Your left side, left side is for unsuccessful trials. 401 00:23:04,560 --> 00:23:07,470 At the end of the session, you've got a total percentage. 402 00:23:07,810 --> 00:23:09,680 Um, you didn't have to do any sticky notes. 403 00:23:09,720 --> 00:23:16,000 Um, another consideration would be, um, another consideration would be probe data. 404 00:23:16,010 --> 00:23:17,850 So probe data is really complex. 405 00:23:17,870 --> 00:23:20,560 Um, we have a whole episode on probe data. 406 00:23:20,960 --> 00:23:25,210 The short version of the story is that any data collection system that you have, 407 00:23:25,250 --> 00:23:26,790 you want your data collection to be. 408 00:23:27,085 --> 00:23:28,685 accurate, reliable and valid. 409 00:23:29,135 --> 00:23:32,695 If you are not measuring accurately, then you're not going to be able 410 00:23:32,695 --> 00:23:35,495 to inform your goals and you're not gonna be able to measure progress. 411 00:23:36,165 --> 00:23:39,365 It's impossible to, in a lot of instances, track every single 412 00:23:39,365 --> 00:23:40,645 instance of an occurrence. 413 00:23:40,965 --> 00:23:44,925 That is when you get into this trouble of not being able to engage with your 414 00:23:44,925 --> 00:23:47,065 client and have a nice connected session. 415 00:23:47,605 --> 00:23:51,895 Um, a potential answer to this problem is probe data where you're only recording 416 00:23:51,905 --> 00:23:54,015 predetermined, a predetermined set. 417 00:23:54,860 --> 00:23:56,370 of a certain number of trials. 418 00:23:57,090 --> 00:24:00,190 The problem with probe data is that it can be really inaccurate. 419 00:24:00,400 --> 00:24:04,280 It can violate that, you know, ideal standard of data collection that's 420 00:24:04,300 --> 00:24:05,570 accurate, reliable, and valid. 421 00:24:06,060 --> 00:24:12,570 The way to mitigate and take probe data in a way that is better is to take more, the 422 00:24:12,570 --> 00:24:14,710 more probes you take, the more accurate. 423 00:24:15,045 --> 00:24:19,285 And if you add a qualifier to that, so let's say you're recording the first 424 00:24:19,285 --> 00:24:23,235 three trials, but you're also recording whether or not it was prompted, you're 425 00:24:23,235 --> 00:24:26,855 also recording how long it, you know, the duration, you're, you're adding 426 00:24:26,855 --> 00:24:30,995 some qualifier onto the probe, the more probes you take, and the more qualifiers 427 00:24:31,005 --> 00:24:32,325 you add, the more accurate it is. 428 00:24:32,335 --> 00:24:36,430 And again, we have an entire hour long episode that reviews the research. 429 00:24:36,730 --> 00:24:38,080 Not that any, it's very dry. 430 00:24:38,090 --> 00:24:40,800 I know it sounds boring, but we really liked it. 431 00:24:41,080 --> 00:24:44,290 Um, so there are a lot of different ways that you can make your probe data 432 00:24:44,290 --> 00:24:45,720 more accurate, reliable and valid. 433 00:24:46,430 --> 00:24:49,710 I think another problem with AAC in particular with data collection 434 00:24:49,710 --> 00:24:52,970 is that it feels cumbersome because you've got this extra device. 435 00:24:52,980 --> 00:24:55,740 So you're like, but I got the device and I've got the student and now I have 436 00:24:55,740 --> 00:24:58,910 these golf counters and a pen and a sticky note and there's all these things. 437 00:24:59,440 --> 00:25:02,070 Um, it can feel really overwhelming. 438 00:25:02,450 --> 00:25:04,410 And I think there is often in a lot of. 439 00:25:04,680 --> 00:25:09,400 A lot of instances, a big temptation to use the internal tracking system. 440 00:25:09,400 --> 00:25:13,080 So a lot of our devices come with internal data collecting data collection 441 00:25:13,090 --> 00:25:17,420 trackers where you can toggle it on and it will record every instance of 442 00:25:17,420 --> 00:25:21,430 a target behavior or every instance of a target communications or every 443 00:25:21,430 --> 00:25:22,960 instance of an icon selection rather. 444 00:25:23,740 --> 00:25:29,440 Those are really tempting, but they are very, they have a lot of limitations. 445 00:25:29,450 --> 00:25:31,890 So the first major, major limitation is ethics. 446 00:25:32,860 --> 00:25:37,880 We have heard from the AAC community that these mechanisms 447 00:25:37,890 --> 00:25:39,320 feel very much like spying. 448 00:25:39,340 --> 00:25:42,140 Imagine if there was someone walking around with you all day, 449 00:25:42,190 --> 00:25:44,390 following you around, listening to every single thing you said. 450 00:25:44,745 --> 00:25:46,425 But didn't tell you that they were listening. 451 00:25:46,955 --> 00:25:50,415 Uh, we need to be, if we're going to use these mechanisms, we need to be extremely 452 00:25:50,415 --> 00:25:54,865 careful about turning them on and off and doing it with informed consent. 453 00:25:54,885 --> 00:25:58,265 And that's informed consent for the AAC user and potentially their 454 00:25:58,265 --> 00:26:00,665 families, depending on their age and all these other kinds of things. 455 00:26:01,965 --> 00:26:04,245 The other thing that we really need to think about with these internal 456 00:26:04,245 --> 00:26:06,245 data collection systems is the law. 457 00:26:06,675 --> 00:26:10,945 So, we could, depending on your state, there is a potential that you are 458 00:26:10,945 --> 00:26:15,935 violating a privacy law, uh, by taking this data and storing it in a cloud. 459 00:26:16,190 --> 00:26:18,250 Um, that is not part of your district. 460 00:26:18,250 --> 00:26:21,310 It could be a violation of FAPE here in Massachusetts. 461 00:26:21,310 --> 00:26:22,620 We have to be very careful about that. 462 00:26:22,620 --> 00:26:25,690 And we have families sign additional permissions, some 463 00:26:25,690 --> 00:26:28,610 schools and some programs I work with won't even do it because it 464 00:26:28,610 --> 00:26:30,130 is too close to some violations. 465 00:26:30,130 --> 00:26:33,460 So check with your administrators, check with your state and make sure 466 00:26:33,460 --> 00:26:37,260 that use of these is even within the provision of what would be considered. 467 00:26:37,390 --> 00:26:42,585 Um, Uh, secure storage of data as part of an educational file. 468 00:26:42,585 --> 00:26:44,315 So that's another consideration there. 469 00:26:44,745 --> 00:26:49,245 Um, the other and last limitation of these internal tracking systems is that 470 00:26:49,255 --> 00:26:50,805 they are going to track everything. 471 00:26:51,625 --> 00:26:55,635 This, these little tiny robots and say, these machines don't know if it's you that 472 00:26:55,635 --> 00:26:58,715 selected the button or the child that's or the student that selected the button. 473 00:26:59,105 --> 00:27:04,355 So if you are using them for short periods of time, because we know we have to turn 474 00:27:04,355 --> 00:27:07,705 them off so that we're not theoretically, you know, following someone around 475 00:27:07,705 --> 00:27:08,985 listening to what they say all day. 476 00:27:09,835 --> 00:27:13,295 While it is on, you want to make sure that you're only capturing 477 00:27:14,025 --> 00:27:15,585 what it is that you're measuring. 478 00:27:15,835 --> 00:27:19,685 So if you're capturing models for a communication partner, you want to make 479 00:27:19,685 --> 00:27:23,145 sure that the student doesn't select a device, doesn't select an icon, or 480 00:27:23,145 --> 00:27:28,530 conversely, if you're using it to measure independent student productions, you have 481 00:27:28,540 --> 00:27:31,440 to make sure you're not providing any prompting, that you're not providing any 482 00:27:31,440 --> 00:27:35,600 modeling on the device, that the tally mark that you're getting is actually 483 00:27:35,620 --> 00:27:36,980 independent productions of the student. 484 00:27:36,980 --> 00:27:39,740 So there are a lot of limitations to those. 485 00:27:40,160 --> 00:27:43,060 Uh, and those are all really important things to consider. 486 00:27:44,485 --> 00:27:45,195 Absolutely. 487 00:27:45,715 --> 00:27:45,895 Yeah. 488 00:27:45,895 --> 00:27:47,115 I hadn't thought about that. 489 00:27:47,125 --> 00:27:48,405 Um, for sure. 490 00:27:48,615 --> 00:27:52,675 And it really, I think in some ways that almost defeats the purpose of, 491 00:27:52,985 --> 00:27:56,185 um, the therapy strategy of modeling. 492 00:27:56,215 --> 00:27:56,535 Right. 493 00:27:56,535 --> 00:28:02,095 So if, if your goal is to model a ton and to really see that 494 00:28:02,095 --> 00:28:05,715 growth through modeling, then you almost would be shooting yourself 495 00:28:05,735 --> 00:28:06,745 in the foot if you used it. 496 00:28:06,745 --> 00:28:07,065 Right. 497 00:28:07,075 --> 00:28:08,175 Like, yeah. 498 00:28:08,400 --> 00:28:14,610 Yeah, well, I want to just ask a follow up question to just, um, 499 00:28:15,050 --> 00:28:16,150 in relation to the gold writing. 500 00:28:16,150 --> 00:28:19,310 So you kind of already answered the initial question that I had. 501 00:28:19,310 --> 00:28:21,410 So I'm just going to add on to the question. 502 00:28:21,730 --> 00:28:25,870 Um, just because again, thinking about our member who's mentoring 503 00:28:25,870 --> 00:28:30,610 someone, um, And really just understanding that relationship between 504 00:28:30,760 --> 00:28:32,620 goal writing and data collection. 505 00:28:33,140 --> 00:28:38,320 How might she help her mentee with goal writing to help 506 00:28:38,330 --> 00:28:40,380 with better data collection? 507 00:28:40,390 --> 00:28:41,360 Does that make sense? 508 00:28:41,640 --> 00:28:42,180 Yeah. 509 00:28:42,680 --> 00:28:45,950 And I think, you know, again, this is a really great question. 510 00:28:45,960 --> 00:28:50,250 And like you said, at the beginning of the episode, to really answer 511 00:28:50,250 --> 00:28:52,570 this question, well, we need a lot more information, right? 512 00:28:52,570 --> 00:28:55,800 Because we don't know if this individual is a complex learner, 513 00:28:55,810 --> 00:28:58,390 we don't know what their goals are. 514 00:28:58,815 --> 00:29:04,155 And the goals and targets are going to have a significant impact on how 515 00:29:04,265 --> 00:29:10,460 progress is monitored, because again, Data collection and goal writing are BFFs. 516 00:29:10,490 --> 00:29:11,700 They cannot be separated. 517 00:29:11,740 --> 00:29:13,350 You cannot do one without the other. 518 00:29:13,390 --> 00:29:14,760 They don't happen in a sequence. 519 00:29:14,760 --> 00:29:16,840 They happen in tandem, and they influence each other, 520 00:29:16,910 --> 00:29:18,690 influence each other continually. 521 00:29:19,130 --> 00:29:22,270 Because as you're monitoring progress, theoretically, if they're making progress, 522 00:29:22,270 --> 00:29:24,070 the goal may need to be adjusted, right? 523 00:29:24,070 --> 00:29:26,960 That's why we have annual IEP meetings, because we're rewriting 524 00:29:26,960 --> 00:29:28,280 goals based on progress. 525 00:29:28,880 --> 00:29:34,960 Um, I think when you're working with a mentee, And you're trying to unpack 526 00:29:34,960 --> 00:29:39,650 some of these concepts, I would go back to the goal first and I would 527 00:29:39,650 --> 00:29:44,900 go back to the target and think about what it is that you're measuring and 528 00:29:45,000 --> 00:29:49,740 all the variables that will influence how that measurement is taken. 529 00:29:50,170 --> 00:29:55,270 Um, that might include all of the things that we've already mentioned, but the 530 00:29:55,270 --> 00:30:01,125 environment, the communication partners, how fleeting the communication target is. 531 00:30:01,485 --> 00:30:05,575 Um, and I also think it's important to have a little bit of forward thinking. 532 00:30:05,595 --> 00:30:08,815 And I know where this is a question about a school environment. 533 00:30:09,215 --> 00:30:13,375 Um, so theoretically you have an entire year under the IEP to take 534 00:30:13,385 --> 00:30:15,495 that, to take that data collection. 535 00:30:16,135 --> 00:30:18,955 But having a really good baseline measurement is also really 536 00:30:18,955 --> 00:30:23,145 important because if you don't know where you started, How do 537 00:30:23,145 --> 00:30:24,415 you know where you're going? 538 00:30:25,055 --> 00:30:29,665 And for some, particularly complex learners, really small steps are really 539 00:30:29,665 --> 00:30:32,125 huge deals and we don't want to miss them. 540 00:30:32,595 --> 00:30:35,805 Uh, we don't want to not give credit where it's due for our students who are 541 00:30:35,805 --> 00:30:39,565 working so hard and the paraprofessionals who are working so hard and the 542 00:30:39,575 --> 00:30:42,645 teachers who are working so hard and the whole team who was working so hard. 543 00:30:42,645 --> 00:30:42,975 Right. 544 00:30:43,355 --> 00:30:48,115 So taking those, I would also be really asking a lot of questions about where 545 00:30:48,115 --> 00:30:52,615 the student is currently and taking really good baseline measurement. 546 00:30:52,800 --> 00:30:56,800 So that you have a strong foundation off of which to judge what 547 00:30:56,800 --> 00:30:58,110 progress was made to begin with. 548 00:30:58,130 --> 00:30:59,260 Did I answer your question? 549 00:31:00,120 --> 00:31:00,770 Yes. 550 00:31:00,960 --> 00:31:01,090 Okay. 551 00:31:01,110 --> 00:31:02,530 So do you want to talk a little bit? 552 00:31:02,530 --> 00:31:09,990 Do we have time to just touch on, um, do you have like favorite ways to, um, 553 00:31:09,990 --> 00:31:15,660 measure baseline or recommendations or strategies that you, like your go to, 554 00:31:16,000 --> 00:31:18,350 um, options, you know, for baseline? 555 00:31:19,105 --> 00:31:20,595 Oh, that's a really, it's a 556 00:31:20,815 --> 00:31:22,175 really good question. 557 00:31:22,305 --> 00:31:28,155 Um, I think not as a standard because it is going to be influenced so much 558 00:31:28,175 --> 00:31:29,795 by the learner and the environment. 559 00:31:30,390 --> 00:31:34,440 Um, I think in a perfect universe, we would take enough baseline measurement 560 00:31:34,450 --> 00:31:37,040 to have a solid understanding that this is exactly where the 561 00:31:37,040 --> 00:31:38,590 student is and not just a bad day. 562 00:31:39,000 --> 00:31:44,040 Um, particularly for more complex learners who might be, you know, presenting with 563 00:31:44,060 --> 00:31:48,290 sleep disturbances or, you know, there might be other things going on in the 564 00:31:48,290 --> 00:31:51,470 child's life that make that one day that you took baseline measurement, not 565 00:31:51,680 --> 00:31:53,030 the best day for baseline measurement. 566 00:31:53,030 --> 00:31:55,810 So in a perfect world, we would have a decent amount of baseline. 567 00:31:56,245 --> 00:31:58,775 A decent amount of measurement at the beginning of treatment to have a 568 00:31:58,775 --> 00:32:02,085 good understanding of where we are, so we can decide where we're going. 569 00:32:02,555 --> 00:32:08,535 Um, I also think that designing data collection systems that feel achievable 570 00:32:08,555 --> 00:32:13,465 and doable is really important because if it's not achievable and doable, then 571 00:32:13,465 --> 00:32:15,890 the data that you collect is going to be. 572 00:32:16,240 --> 00:32:16,920 Inaccurate. 573 00:32:17,330 --> 00:32:20,690 Uh, in one of our data collection courses, we talk about this a little bit in a 574 00:32:20,690 --> 00:32:24,040 little bit more depth, but there's an expression, garbage in, garbage out. 575 00:32:24,760 --> 00:32:29,560 So if your data collection is inaccurate, that's going to inform your progress. 576 00:32:29,970 --> 00:32:33,820 In an inaccurate way, which is going to lead to inaccurate decision 577 00:32:33,820 --> 00:32:39,520 making and clinical reflection, um, and potentially poor choices for 578 00:32:39,520 --> 00:32:41,150 implementation and intervention. 579 00:32:41,610 --> 00:32:46,560 So, really making sure that we hold data collection strategies that are, 580 00:32:46,560 --> 00:32:50,930 um, Accurate, reliable, and valid at the center is really, really important. 581 00:32:51,110 --> 00:32:54,860 And there's really no gold standard because it's such a customized experience. 582 00:32:55,250 --> 00:32:58,750 We also have a, we have a handout on our website that I will include in the 583 00:32:58,750 --> 00:33:02,540 show notes as well on what accurate, reliable, and valid data means. 584 00:33:02,960 --> 00:33:07,470 Um, and again, referring people back to our original, you know, some of our 585 00:33:07,470 --> 00:33:11,330 previous work in data collection, just because I recognize that this is a very, 586 00:33:11,470 --> 00:33:14,780 a very nuanced, very nuanced conversation. 587 00:33:15,665 --> 00:33:19,015 Yes, well, and just, you know, one takeaway for me, just as you were 588 00:33:19,015 --> 00:33:23,225 talking is to really think about representative samples, right? 589 00:33:23,415 --> 00:33:26,875 And when we think about this, whether it's a speech sample for an 590 00:33:26,885 --> 00:33:30,925 Arctic kid or language sample for a child with a developmental delay. 591 00:33:30,955 --> 00:33:32,755 I mean, it doesn't matter what the. 592 00:33:33,470 --> 00:33:38,820 Uh, situation is we have to make sure that we are doing what we can to make 593 00:33:38,820 --> 00:33:44,940 sure that we are sampling, um, their speech, their device usage, whatever in 594 00:33:45,100 --> 00:33:49,210 the best way possible, but also yielding the most representative sample possible. 595 00:33:49,210 --> 00:33:49,670 And so. 596 00:33:50,175 --> 00:33:54,085 It might take more than one trial, right, to get there. 597 00:33:54,325 --> 00:33:57,845 And because, like you said, there's so many variables that could impact that 598 00:33:57,865 --> 00:34:02,345 individual's willingness to participate or willingness to show what they do 599 00:34:02,345 --> 00:34:04,475 know or what they are capable of doing. 600 00:34:04,485 --> 00:34:07,285 So, um, so, yeah, that's important. 601 00:34:07,285 --> 00:34:12,915 I think sometimes, you know, with AAC, we, we tend to maybe think, um, differently, 602 00:34:12,915 --> 00:34:15,775 or, or we don't use sometimes like just the basic knowledge that we already 603 00:34:15,775 --> 00:34:20,025 have about like, yeah, in the same way that this applies to X, Y, Z, it's 604 00:34:20,025 --> 00:34:22,685 going to also apply for our AAC users. 605 00:34:22,955 --> 00:34:25,785 Um, there's maybe, like you said, nuances or different things that we 606 00:34:25,795 --> 00:34:31,525 have to take into consideration, but it's still, there's some basics that. 607 00:34:32,010 --> 00:34:34,780 Are just foundational, right? 608 00:34:36,220 --> 00:34:36,860 Totally agree. 609 00:34:37,610 --> 00:34:38,190 Totally agree. 610 00:34:38,570 --> 00:34:42,940 So, I mean, I think, I think I really appreciate the literature 611 00:34:42,940 --> 00:34:44,090 that you brought to the table. 612 00:34:44,110 --> 00:34:46,000 I know I shared quite a bit, but I couldn't help myself 613 00:34:46,000 --> 00:34:47,310 because this is my area. 614 00:34:47,420 --> 00:34:48,820 This is my clinical area. 615 00:34:49,300 --> 00:34:49,920 I love 616 00:34:50,280 --> 00:34:51,000 learning from you. 617 00:34:51,020 --> 00:34:51,710 So great. 618 00:34:51,710 --> 00:34:57,090 Um, we will link every all of the additional resources 619 00:34:57,100 --> 00:34:58,370 in the show notes and Apollo. 620 00:34:58,370 --> 00:35:00,210 Was there anything else that you wanted to share? 621 00:35:00,990 --> 00:35:02,090 No, that's it. 622 00:35:02,210 --> 00:35:03,590 That's all I had for today. 623 00:35:04,085 --> 00:35:06,985 And to the listener who wrote in this question, thank 624 00:35:06,985 --> 00:35:08,635 you so much for writing in. 625 00:35:08,905 --> 00:35:13,695 We hope we did it justice, um, on, you know, in terms of what you shared. 626 00:35:13,745 --> 00:35:16,645 Anyone out there who's listening, if you have a question for us and you're 627 00:35:16,645 --> 00:35:21,145 a member, Please write in, we would love to read your questions and do a 628 00:35:21,145 --> 00:35:25,135 little literature search for you and discuss your clinical case on the air. 629 00:35:25,565 --> 00:35:26,685 Um, Dr. 630 00:35:26,685 --> 00:35:28,785 Anupama Moomy, thank you so much for being here. 631 00:35:28,785 --> 00:35:32,585 This was really wonderful and we look forward to the next 632 00:35:32,605 --> 00:35:34,685 iteration of SLPD On Demand. 633 00:35:38,297 --> 00:35:41,867 Thank you so much for joining us in today's episode, as always, you 634 00:35:41,867 --> 00:35:44,077 can use this episode for ASHA CEUs. 635 00:35:44,387 --> 00:35:48,257 You can also potentially use this episode for other credits, depending on 636 00:35:48,257 --> 00:35:49,907 the regulations of your governing body. 637 00:35:50,387 --> 00:35:52,907 To determine if this episode will count towards professional 638 00:35:52,907 --> 00:35:54,347 development in your area of study. 639 00:35:54,617 --> 00:35:57,947 Please check in with your governing bodies or you can go to our website, 640 00:35:57,947 --> 00:36:03,227 www.slpnerdcast.com all of the references and information listed 641 00:36:03,227 --> 00:36:05,777 throughout the course of the episode will be listed in the show notes. 642 00:36:05,957 --> 00:36:10,847 And as always, if you have any questions, please email us at info@slpnerdcast.com 643 00:36:11,957 --> 00:36:15,167 thank you so much for joining us and we hope to welcome you back here again soon.