Akwai buƙatar koyon da ya shafi ɗalibai (SCL) a manyan cibiyoyin ilimi, gami da ilimin hakora. Duk da haka, SCL tana da iyakacin amfani a ilimin hakora. Saboda haka, wannan binciken yana da nufin haɓaka amfani da SCL a fannin ilimin hakora ta hanyar amfani da fasahar koyon injin yanke shawara (ML) don tsara salon koyo da aka fi so (LS) da dabarun koyo masu dacewa (IS) na ɗaliban hakori a matsayin kayan aiki mai amfani don haɓaka jagororin IS. Hanyoyi masu kyau ga ɗaliban hakori.
Jimillar ɗaliban likitancin hakori 255 daga Jami'ar Malaya sun kammala tambayoyin da aka gyara na Index of Learning Styles (m-ILS), wanda ya ƙunshi abubuwa 44 don rarraba su cikin LSs ɗinsu. Ana amfani da bayanan da aka tattara (wanda ake kira bayanai) a cikin koyo na bishiyoyi masu kulawa don daidaita salon koyo na ɗalibai ta atomatik zuwa mafi dacewa IS. Sannan ana tantance daidaiton kayan aikin ba da shawarar IS bisa ga koyo na inji.
Amfani da samfuran bishiyoyin yanke shawara a cikin tsarin taswirar atomatik tsakanin LS (shigarwa) da IS (fitarwa mai ma'ana) yana ba da damar samun jerin dabarun koyo masu dacewa nan take ga kowane ɗalibi na likitan hakori. Kayan aikin shawarar IS ya nuna cikakken daidaito da tunawa da daidaiton samfurin gabaɗaya, yana nuna cewa daidaitawar LS da IS yana da kyakkyawan fahimta da takamaiman bayani.
Kayan aikin ba da shawara na IS bisa ga bishiyar yanke shawara ta ML ya tabbatar da ikonsa na daidaita salon koyo na ɗaliban hakori daidai da dabarun koyo masu dacewa. Wannan kayan aikin yana ba da zaɓuɓɓuka masu ƙarfi don tsara darussa ko kayan aiki masu mahimmanci ga ɗalibai waɗanda za su iya haɓaka ƙwarewar koyo na ɗalibai.
Koyarwa da koyo muhimman ayyuka ne a cibiyoyin ilimi. Lokacin ƙirƙirar tsarin ilimin sana'a mai inganci, yana da mahimmanci a mai da hankali kan buƙatun ilmantarwa na ɗalibai. Ana iya tantance hulɗar da ke tsakanin ɗalibai da yanayin koyo ta hanyar LS ɗinsu. Bincike ya nuna cewa rashin daidaiton da malamai suka yi niyya tsakanin LS da IS na ɗalibai na iya haifar da mummunan sakamako ga koyon ɗalibai, kamar raguwar hankali da kwarin gwiwa. Wannan zai shafi aikin ɗalibai a kaikaice [1,2].
IS wata hanya ce da malamai ke amfani da ita don isar da ilimi da ƙwarewa ga ɗalibai, gami da taimaka wa ɗalibai su koya [3]. Gabaɗaya, malamai masu kyau suna tsara dabarun koyarwa ko IS waɗanda suka fi dacewa da matakin ilimin ɗalibansu, ra'ayoyin da suke koyo, da kuma matakin koyo. A ka'ida, idan LS da IS suka yi daidai, ɗalibai za su iya tsarawa da amfani da takamaiman tsarin ƙwarewa don koyo yadda ya kamata. Yawanci, tsarin darasi ya haɗa da sauye-sauye da yawa tsakanin matakai, kamar daga koyarwa zuwa aikin jagora ko daga aikin jagora zuwa aikin kai tsaye. Da wannan a zuciya, malamai masu inganci galibi suna tsara koyarwa da nufin gina ilimin ɗalibai da ƙwarewarsu [4].
Bukatar SCL na ƙaruwa a manyan cibiyoyin ilimi, ciki har da likitan hakori. An tsara dabarun SCL don biyan buƙatun ilmantarwa na ɗalibai. Ana iya cimma wannan, misali, idan ɗalibai suka shiga cikin ayyukan ilmantarwa kuma malamai suka yi aiki a matsayin masu gudanarwa kuma suna da alhakin bayar da ra'ayoyi masu mahimmanci. Ana cewa samar da kayan koyo da ayyukan da suka dace da matakin ilimi ko fifikon ɗalibai na iya inganta yanayin koyo na ɗalibai da kuma haɓaka ƙwarewar koyo mai kyau [5].
Gabaɗaya dai, tsarin koyon ɗaliban haƙori yana tasiri ne ta hanyar hanyoyin asibiti daban-daban da ake buƙatar su yi da kuma yanayin asibiti inda suke haɓaka ƙwarewar hulɗa da mutane masu inganci. Manufar horarwar ita ce baiwa ɗalibai damar haɗa ilimin asali na haƙori da ƙwarewar asibiti na haƙori da kuma amfani da ilimin da aka samu ga sabbin yanayi na asibiti [6, 7]. Binciken farko kan alaƙar da ke tsakanin LS da IS ya gano cewa daidaita dabarun koyo da aka tsara zuwa ga LS da aka fi so zai taimaka wajen inganta tsarin ilimi [8]. Marubutan sun kuma ba da shawarar amfani da hanyoyi daban-daban na koyarwa da kimantawa don daidaitawa da ilmantarwa da buƙatun ɗalibai.
Malamai suna amfana daga amfani da ilimin LS don taimaka musu tsara, haɓakawa, da aiwatar da koyarwa wanda zai haɓaka samun ƙarin ilimi da fahimtar batun. Masu bincike sun ƙirƙiro kayan aikin tantance LS da yawa, kamar Samfurin Koyon Kwarewa na Kolb, Samfurin Salon Koyo na Felder-Silverman (FSLSM), da Samfurin Fleming VAK/VARK [5, 9, 10]. A cewar wallafe-wallafen, waɗannan samfuran koyo sune samfuran koyo da aka fi amfani da su kuma aka fi nazari a kansu. A cikin aikin bincike na yanzu, ana amfani da FSLSM don tantance LS tsakanin ɗaliban hakori.
FSLSM wani tsari ne da ake amfani da shi sosai don kimanta koyo mai daidaitawa a fannin injiniyanci. Akwai ayyuka da yawa da aka buga a kimiyyar lafiya (gami da magani, aikin jinya, kantin magani da kuma likitan hakori) waɗanda za a iya samu ta amfani da samfuran FSLSM [5, 11, 12, 13]. Kayan aikin da ake amfani da shi don auna girman LS a cikin FLSM ana kiransa Fihirisar Salon Koyo (ILS) [8], wanda ya ƙunshi abubuwa 44 waɗanda ke tantance girma huɗu na LS: sarrafawa (mai aiki/mai tunani), fahimta (fahimta/fahimta), shigarwa (na gani). / magana) da fahimta (jeri/duniya) [14].
Kamar yadda aka nuna a Hoto na 1, kowane girman FSLSM yana da fifiko mafi rinjaye. Misali, a fannin sarrafawa, ɗaliban da ke da LS mai "aiki" sun fi son sarrafa bayanai ta hanyar mu'amala kai tsaye da kayan koyo, koyo ta hanyar aikatawa, kuma suna son koyo a cikin rukuni. LS mai "tunani" yana nufin koyo ta hanyar tunani kuma sun fi son yin aiki su kaɗai. Ana iya raba girman "fahimta" na LS zuwa "ji" da/ko "fahimta." Ɗaliban "ji" sun fi son ƙarin bayani da hanyoyin aiki, suna da alaƙa da gaskiya idan aka kwatanta da ɗaliban "fahimta" waɗanda suka fi son abu mai sauƙi kuma sun fi ƙirƙira da ƙirƙira a yanayi. Girman "shigarwa" na LS ya ƙunshi ɗaliban "na gani" da "na magana". Mutanen da ke da LS mai "gani" sun fi son koyo ta hanyar nunin gani (kamar zane-zane, bidiyo, ko nunin kai tsaye), yayin da mutanen da ke da LS mai "fahimta" sun fi son koyo ta kalmomi a cikin bayanin rubutu ko na baki. Don "fahimtar" girman LS, irin waɗannan ɗaliban za a iya raba su zuwa "jeri" da "na duniya". "Masu koyon layi sun fi son tsarin tunani mai layi sannan su koya mataki-mataki, yayin da ɗaliban duniya ke da tsarin tunani mai zurfi kuma koyaushe suna da fahimtar abin da suke koya."
Kwanan nan, masu bincike da yawa sun fara bincika hanyoyin gano bayanai ta atomatik, gami da haɓaka sabbin algorithms da samfura waɗanda ke iya fassara adadi mai yawa na bayanai [15, 16]. Dangane da bayanan da aka bayar, ML mai kulawa (koyon injin) yana iya samar da alamu da hasashe waɗanda ke hasashen sakamakon nan gaba bisa ga gina algorithms [17]. A taƙaice, dabarun koyon injin da aka sa ido suna sarrafa bayanan shigarwa da horar da algorithms. Sannan yana samar da kewayon da ke rarrabawa ko annabta sakamakon bisa ga yanayi iri ɗaya na bayanan shigarwa da aka bayar. Babban fa'idar algorithms na koyon injin da aka sa ido a kai shine ikonsa na kafa sakamako mai kyau da ake so [17].
Ta hanyar amfani da hanyoyin da bayanai ke amfani da su da kuma samfuran sarrafa bishiyoyi, ana iya gano LS ta atomatik. An ruwaito cewa ana amfani da bishiyoyin yanke shawara sosai a shirye-shiryen horarwa a fannoni daban-daban, ciki har da kimiyyar lafiya [18, 19]. A cikin wannan binciken, masu haɓaka tsarin sun horar da samfurin musamman don gano LS na ɗalibai da kuma ba da shawarar mafi kyawun IS a gare su.
Manufar wannan binciken ita ce ƙirƙirar dabarun isar da IS bisa ga LS na ɗalibai da kuma amfani da hanyar SCL ta hanyar ƙirƙirar kayan aikin shawarwarin IS da aka tsara zuwa LS. An nuna tsarin ƙirar kayan aikin shawarwarin IS a matsayin dabarun hanyar SCL a cikin Hoto na 1. An raba kayan aikin shawarwarin IS zuwa sassa biyu, gami da tsarin rarrabuwar LS ta amfani da ILS da kuma nunin IS mafi dacewa ga ɗalibai.
Musamman ma, halayen kayan aikin ba da shawara kan tsaron bayanai sun haɗa da amfani da fasahar yanar gizo da kuma amfani da na'urar koyon injin yanke shawara. Masu haɓaka tsarin suna inganta ƙwarewar mai amfani da motsi ta hanyar daidaita su da na'urorin hannu kamar wayoyin hannu da kwamfutar hannu.
An gudanar da gwajin a matakai biyu kuma ɗalibai daga Faculty of Dentistry a Jami'ar Malaya sun shiga bisa son rai. Mahalarta sun amsa tambayoyin m-ILS na ɗalibi a kan layi na ɗalibi a fannin likitancin hakori a Turanci. A matakin farko, an yi amfani da bayanan ɗalibai 50 don horar da tsarin koyon injin yanke shawara. A mataki na biyu na tsarin haɓakawa, an yi amfani da bayanan ɗalibai 255 don inganta daidaiton kayan aikin da aka ƙirƙira.
Duk mahalarta suna samun bayani ta yanar gizo a farkon kowane mataki, ya danganta da shekarar karatu, ta hanyar Microsoft Teams. An bayyana manufar binciken kuma an sami izini mai kyau. An ba wa duk mahalarta hanyar haɗi don shiga m-ILS. An umurci kowane ɗalibi ya amsa duk abubuwa 44 da ke kan tambayoyin. An ba su mako guda don kammala ILS da aka gyara a lokaci da wuri da ya dace da su a lokacin hutun zangon karatu kafin fara zangon karatu. M-ILS ya dogara ne akan kayan aikin ILS na asali kuma an gyara shi don ɗaliban hakori. Kamar ILS na asali, ya ƙunshi abubuwa 44 da aka rarraba daidai (a, b), tare da abubuwa 11 kowannensu, waɗanda ake amfani da su don tantance fannoni na kowane girman FSLSM.
A lokacin matakan farko na ƙirƙirar kayan aiki, masu binciken sun yi bayanin taswirar da hannu ta amfani da bayanai na ɗaliban likitan hakori 50. A cewar FSLM, tsarin yana ba da jimlar amsoshin "a" da "b". Ga kowane girma, idan ɗalibi ya zaɓi "a" a matsayin amsa, ana rarraba LS a matsayin Active/Perceptual/Visual/Sequential, kuma idan ɗalibi ya zaɓi "b" a matsayin amsa, za a rarraba ɗalibin a matsayin Mai Tunani/Ituitive/Linguistic. / mai koyo na duniya.
Bayan daidaita tsarin aiki tsakanin masu binciken ilimin hakori da masu haɓaka tsarin, an zaɓi tambayoyi bisa ga yankin FLSSM kuma an saka su cikin samfurin ML don annabta LS na kowane ɗalibi. "Shara a ciki, a fitar da shara" sanannen magana ce a fagen koyon injina, tare da mai da hankali kan ingancin bayanai. Ingancin bayanan shigarwa yana ƙayyade daidaito da daidaito na samfurin koyon injina. A lokacin matakin injiniyan fasali, an ƙirƙiri sabon saitin fasali wanda shine jimlar amsoshin "a" da "b" bisa ga FLSSM. An ba da lambobin tantance matsayin magunguna a cikin Tebur 1.
A ƙididdige maki bisa ga amsoshin sannan a tantance LS na ɗalibi. Ga kowane ɗalibi, kewayon maki yana daga 1 zuwa 11. Maki daga 1 zuwa 3 yana nuna daidaiton fifikon koyo a cikin girma ɗaya, kuma maki daga 5 zuwa 7 yana nuna fifikon matsakaici, yana nuna cewa ɗalibai suna fifita yanayi ɗaya don koyar da wasu. Wani bambanci akan girma ɗaya shine cewa maki daga 9 zuwa 11 yana nuna fifikon ƙarfi ga gefe ɗaya ko ɗayan [8].
Ga kowane girma, an haɗa magunguna zuwa "mai aiki", "mai tunani" da "daidaitacce". Misali, lokacin da ɗalibi ya amsa "a" sau da yawa fiye da "b" akan wani abu da aka ƙayyade kuma maki/makiyarsa ta wuce iyakar 5 ga wani abu da ke wakiltar girman LS na Sarrafawa, shi/ita tana cikin yankin LS na "mai aiki". Duk da haka, an rarraba ɗalibai a matsayin LS na "mai tunani" lokacin da suka zaɓi "b" fiye da "a" a cikin takamaiman tambayoyi 11 (Tebur 1) kuma suka sami maki sama da 5. A ƙarshe, ɗalibin yana cikin yanayin "daidaituwa." Idan maki bai wuce maki 5 ba, to wannan "tsari" ne na LS. An maimaita tsarin rarrabuwa don sauran ma'aunin LS, wato fahimta (mai aiki/mai tunani), shigarwa (na gani/na magana), da fahimta (jeri/na duniya).
Samfuran bishiyoyin yanke shawara na iya amfani da ƙananan siffofi da ƙa'idodin yanke shawara daban-daban a matakai daban-daban na tsarin rarrabuwa. Ana ɗaukarsa a matsayin kayan aikin rarrabuwa da hasashe mai shahara. Ana iya wakilta shi ta amfani da tsarin itace kamar jadawalin kwarara [20], inda akwai ƙananan ƙwayoyin ciki waɗanda ke wakiltar gwaje-gwaje ta hanyar siffa, kowane reshe yana wakiltar sakamakon gwaji, da kuma kowane ƙananan ƙwayoyin ganye (ƙulli na ganye) wanda ke ɗauke da lakabin aji.
An ƙirƙiri wani shiri mai sauƙi bisa ƙa'ida don yin maki da kuma yin bayanin LS na kowane ɗalibi ta atomatik bisa ga amsoshinsu. Mai tushen ƙa'ida yana ɗaukar siffar bayanin IF, inda "IF" ke bayyana abin da ke haifar da shi kuma "THEN" yana ƙayyade aikin da za a yi, misali: "Idan X ya faru, to yi Y" (Liu et al., 2014). Idan saitin bayanai ya nuna alaƙa kuma an horar da samfurin bishiyar yanke shawara yadda ya kamata kuma an kimanta shi, wannan hanyar na iya zama hanya mai tasiri don sarrafa tsarin daidaita LS da IS ta atomatik.
A mataki na biyu na haɓakawa, an ƙara bayanan zuwa 255 don inganta daidaiton kayan aikin ba da shawara. An raba bayanan a cikin rabo na 1:4. An yi amfani da kashi 25% (64) na bayanan don saitin gwajin, kuma an yi amfani da sauran kashi 75% (191) azaman saitin horo (Hoto na 2). Ana buƙatar raba bayanan don hana a horar da samfurin da gwada shi akan saitin bayanai iri ɗaya, wanda zai iya sa samfurin ya tuna maimakon koyo. An horar da samfurin akan saitin horo kuma yana kimanta aikinsa akan saitin gwaji - bayanan da samfurin bai taɓa gani ba a da.
Da zarar an ƙirƙiri kayan aikin IS, aikace-aikacen zai iya rarraba LS bisa ga amsoshin ɗaliban hakori ta hanyar hanyar sadarwa ta yanar gizo. An gina tsarin kayan aikin ba da shawara kan tsaro na bayanai ta yanar gizo ta amfani da harshen shirye-shiryen Python ta amfani da tsarin Django a matsayin ƙarshen baya. Tebur na 2 ya lissafa ɗakunan karatu da aka yi amfani da su wajen haɓaka wannan tsarin.
Ana aika bayanan zuwa ga samfurin bishiyar yanke shawara don ƙididdigewa da kuma cire amsoshin ɗalibai don rarraba ma'aunin LS na ɗalibai ta atomatik.
Ana amfani da ma'aunin rikicewa don kimanta daidaiton tsarin koyon injin yanke shawara akan saitin bayanai da aka bayar. A lokaci guda, yana kimanta aikin samfurin rarrabuwa. Yana taƙaita hasashen samfurin kuma yana kwatanta su da ainihin alamun bayanai. Sakamakon kimantawa ya dogara ne akan ƙima huɗu daban-daban: Gaskiya Mai Kyau (TP) - samfurin ya annabta nau'in tabbatacce daidai, Ƙarya Mai Kyau (FP) - samfurin ya annabta nau'in tabbatacce, amma ainihin lakabin ya kasance mara kyau, Gaskiya Mai Kyau (TN) - samfurin ya annabta nau'in mummunan daidai, da ƙarya mara kyau (FN) - Samfurin yana annabta nau'in mummunan daidai, amma ainihin lakabin yana da kyau.
Ana amfani da waɗannan dabi'u don ƙididdige ma'aunin aiki daban-daban na samfurin rarrabuwa na scikit-learn a cikin Python, wato daidaito, daidaito, tunawa, da maki F1. Ga misalai:
Tunatarwa (ko kuma jin daɗi) yana auna ikon samfurin na rarraba LS na ɗalibi daidai bayan amsa tambayoyin m-ILS.
Ana kiran takamaiman sakamako a matsayin ƙimar gaske ta rashin lafiya. Kamar yadda kuke gani daga dabarar da ke sama, wannan ya kamata ya zama rabon gaskiya ta rashin lafiya (TN) zuwa gaskiya ta rashin lafiya da kuma ƙarya ta tabbatacce (FP). A matsayin wani ɓangare na kayan aikin da aka ba da shawarar don rarraba magungunan ɗalibai, ya kamata ya zama yana da ikon gano daidai.
Asalin bayanan ɗalibai 50 da aka yi amfani da su don horar da samfurin ML na bishiyar yanke shawara ya nuna ƙarancin daidaito saboda kuskuren ɗan adam a cikin bayanan (Tebur 3). Bayan ƙirƙirar wani shiri mai sauƙi bisa ƙa'ida don ƙididdige maki LS da bayanan ɗalibai ta atomatik, an yi amfani da ƙarin adadin bayanai (255) don horarwa da gwada tsarin mai ba da shawara.
A cikin matrix ɗin rikice-rikice na azuzuwan da yawa, abubuwan diagonal suna wakiltar adadin hasashen da ya dace ga kowane nau'in LS (Hoto na 4). Ta amfani da samfurin bishiyar yanke shawara, an yi hasashen jimillar samfura 64 daidai. Don haka, a cikin wannan binciken, abubuwan diagonal suna nuna sakamakon da ake tsammani, yana nuna cewa samfurin yana aiki da kyau kuma yana annabta alamar ajin daidai ga kowane rarrabuwar LS. Don haka, daidaiton kayan aikin ba da shawara gabaɗaya shine 100%.
An nuna ƙimar daidaito, daidaito, tunawa, da maki F1 a cikin Hoto na 5. Ga tsarin shawarwari ta amfani da samfurin bishiyar yanke shawara, maki F1 ɗinsa "cikakke" ne 1.0, yana nuna cikakken daidaito da tunawa, yana nuna mahimmancin hankali da ƙimar takamaiman bayanai.
Hoto na 6 ya nuna yadda ake nuna samfurin bishiyar yanke shawara bayan an kammala horo da gwaji. A cikin kwatancen gefe-gefe, samfurin bishiyar yanke shawara da aka horar da shi tare da ƙarancin fasaloli ya nuna daidaito mafi girma da sauƙin hangen nesa na samfuri. Wannan yana nuna cewa injiniyan fasaloli da ke haifar da rage fasali muhimmin mataki ne na inganta aikin samfuri.
Ta hanyar amfani da tsarin yanke shawara da aka kula da shi, ana samar da taswirar tsakanin LS (shigarwa) da IS (fitarwa mai ma'ana) ta atomatik kuma tana ɗauke da cikakkun bayanai ga kowane LS.
Sakamakon ya nuna cewa kashi 34.9% na ɗaliban 255 sun fi son zaɓin LS ɗaya (1). Yawancinsu (54.3%) suna da fifikon LS guda biyu ko fiye. Kashi 12.2% na ɗalibai sun lura cewa LS yana da daidaito sosai (Tebur 4). Baya ga manyan LS guda takwas, akwai haɗakar rarrabuwar LS guda 34 ga ɗaliban haƙoran Jami'ar Malaya. Daga cikinsu, fahimta, hangen nesa, da haɗin fahimta da hangen nesa sune manyan LS da ɗalibai suka ruwaito (Hoto na 7).
Kamar yadda aka gani daga Tebur na 4, yawancin ɗalibai suna da mafi yawan jijiyoyi (13.7%) ko kuma gani (8.6%) LS. An ruwaito cewa kashi 12.2% na ɗalibai sun haɗa fahimta da gani (jijiyoyin gani-gani). Waɗannan binciken sun nuna cewa ɗalibai sun fi son koyo da tunawa ta hanyar hanyoyin da aka kafa, bin takamaiman hanyoyin da aka tsara, kuma suna da hankali a yanayi. A lokaci guda, suna jin daɗin koyo ta hanyar duba (ta amfani da zane-zane, da sauransu) kuma suna son tattaunawa da amfani da bayanai a cikin ƙungiyoyi ko kuma su kaɗai.
Wannan binciken ya ba da taƙaitaccen bayani game da dabarun koyon injina da ake amfani da su a haƙar bayanai, tare da mai da hankali kan hasashen LS na ɗalibai nan take da kuma daidai da kuma ba da shawarar IS mai dacewa. Amfani da samfurin bishiyar yanke shawara ya gano abubuwan da suka fi alaƙa da rayuwarsu da ƙwarewar ilimi. Tsarin koyon injin ne mai kulawa wanda ke amfani da tsarin itace don rarraba bayanai ta hanyar raba saitin bayanai zuwa ƙananan rukunoni bisa ga wasu sharuɗɗa. Yana aiki ta hanyar raba bayanan shigarwa zuwa ƙananan rukunoni bisa ga ƙimar ɗayan fasalulluka na shigarwa na kowane kumburi na ciki har sai an yanke shawara a wurin ƙullin ganye.
Lambobin ciki na bishiyar yanke shawara suna wakiltar mafita bisa ga halayen shigarwar matsalar m-ILS, kuma lambobin ganye suna wakiltar hasashen rarrabuwa na ƙarshe na LS. A cikin binciken, yana da sauƙin fahimtar tsarin bishiyoyin yanke shawara waɗanda ke bayyana da kuma hango tsarin yanke shawara ta hanyar duba alaƙar da ke tsakanin fasalulluka na shigarwa da hasashen fitarwa.
A fannin kimiyyar kwamfuta da injiniyanci, ana amfani da tsarin koyon injina sosai don hasashen nasarar ɗalibai bisa ga sakamakon jarrabawar shiga makarantarsu [21], bayanan alƙaluma, da kuma ɗabi'ar koyo [22]. Bincike ya nuna cewa tsarin ya yi hasashen nasarar ɗalibai daidai kuma ya taimaka musu wajen gano ɗaliban da ke cikin haɗarin fuskantar matsalolin ilimi.
An ruwaito cewa ana amfani da algorithms na ML wajen haɓaka na'urorin kwaikwayo na marasa lafiya na kama-da-wane don horar da haƙori. Na'urar kwaikwayo tana da ikon sake maimaita martanin ilimin halittar marasa lafiya na gaske daidai kuma ana iya amfani da ita don horar da ɗaliban haƙori a cikin yanayi mai aminci da kulawa [23]. Wasu bincike da dama sun nuna cewa algorithms na koyon injina na iya inganta inganci da ingancin ilimin haƙori da likitanci da kula da marasa lafiya. An yi amfani da algorithms na koyon injina don taimakawa wajen gano cututtukan haƙori bisa ga saitin bayanai kamar alamu da halayen marasa lafiya [24, 25]. Yayin da wasu bincike suka bincika amfani da algorithms na koyon injina don yin ayyuka kamar hasashen sakamakon marasa lafiya, gano marasa lafiya masu haɗari, ƙirƙirar tsare-tsaren magani na musamman [26], maganin periodontal [27], da maganin caries [25].
Duk da cewa an buga rahotanni kan amfani da ilimin injina a fannin ilimin hakora, aikace-aikacensa a fannin ilimin hakora ya kasance yana da iyaka. Saboda haka, wannan binciken ya yi nufin amfani da samfurin yanke shawara don gano abubuwan da suka fi alaƙa da LS da IS a tsakanin ɗaliban likitan hakori.
Sakamakon wannan binciken ya nuna cewa kayan aikin ba da shawara da aka haɓaka yana da daidaito mai yawa da cikakkiyar daidaito, yana nuna cewa malamai za su iya amfana daga wannan kayan aikin. Ta amfani da tsarin rarraba bayanai, yana iya samar da shawarwari na musamman da inganta gogewa da sakamako na ilimi ga masu ilimi da ɗalibai. Daga cikinsu, bayanan da aka samu ta hanyar kayan aikin ba da shawara na iya warware rikice-rikice tsakanin hanyoyin koyarwa da malamai suka fi so da buƙatun ilmantarwa na ɗalibai. Misali, saboda fitar da kayan aikin ba da shawara ta atomatik, lokacin da ake buƙata don gano IP na ɗalibi da kuma daidaita shi da IP ɗin da ya dace zai ragu sosai. Ta wannan hanyar, ana iya tsara ayyukan horo da kayan horo masu dacewa. Wannan yana taimakawa wajen haɓaka ɗabi'ar koyo mai kyau ta ɗalibai da ikon mai da hankali. Wani bincike ya ruwaito cewa samar wa ɗalibai kayan koyo da ayyukan koyo waɗanda suka dace da LS ɗin da suka fi so na iya taimaka wa ɗalibai su haɗa kai, su aiwatar, da kuma jin daɗin koyo ta hanyoyi da yawa don cimma babban damar [12]. Bincike ya kuma nuna cewa baya ga inganta halartar ɗalibai a cikin aji, fahimtar tsarin koyo na ɗalibai kuma yana taka muhimmiyar rawa wajen inganta ayyukan koyarwa da sadarwa da ɗalibai [28, 29].
Duk da haka, kamar kowace fasaha ta zamani, akwai matsaloli da iyakoki. Waɗannan sun haɗa da batutuwan da suka shafi sirrin bayanai, son zuciya da adalci, da ƙwarewar ƙwararru da albarkatun da ake buƙata don haɓakawa da aiwatar da algorithms na koyon injina a cikin ilimin hakori; Duk da haka, ƙaruwar sha'awa da bincike a wannan fanni yana nuna cewa fasahar koyon injina na iya yin tasiri mai kyau ga ilimin hakori da ayyukan hakori.
Sakamakon wannan binciken ya nuna cewa rabin ɗaliban likitan hakori suna da halin "fahimtar" magunguna. Wannan nau'in ɗalibi yana da fifiko ga gaskiya da misalai na zahiri, yanayin aiki, haƙuri don cikakkun bayanai, da kuma fifikon LS na "gani", inda ɗalibai suka fi son amfani da hotuna, zane-zane, launuka, da taswira don isar da ra'ayoyi da tunani. Sakamakon yanzu ya yi daidai da sauran nazarin da ake amfani da ILS don tantance LS a cikin ɗaliban likitan hakori da na likitanci, waɗanda yawancinsu suna da halaye na LS na gani da na gani [12, 30]. Dalmolin et al sun ba da shawarar cewa sanar da ɗalibai game da LS ɗinsu yana ba su damar isa ga ƙarfin koyo. Masu bincike sun yi jayayya cewa lokacin da malamai suka fahimci tsarin ilimin ɗalibai sosai, ana iya aiwatar da hanyoyi da ayyuka daban-daban na koyarwa waɗanda za su inganta aikin ɗalibai da ƙwarewar koyo [12, 31, 32]. Wasu nazarin sun nuna cewa daidaita LS na ɗalibai kuma yana nuna ci gaba a cikin ƙwarewar koyo da aikin ɗalibai bayan canza salon koyo don dacewa da LS ɗinsu [13, 33].
Ra'ayoyin malamai na iya bambanta game da aiwatar da dabarun koyarwa bisa ga ƙwarewar ilmantarwa na ɗalibai. Duk da cewa wasu suna ganin fa'idodin wannan hanyar, gami da damar haɓaka ƙwararru, jagoranci, da tallafin al'umma, wasu na iya damuwa da lokaci da tallafin cibiyoyi. Kokarin daidaita abubuwa shine mabuɗin ƙirƙirar ɗabi'a mai mayar da hankali kan ɗalibai. Hukumomin ilimi na gaba, kamar masu gudanar da jami'o'i, na iya taka muhimmiyar rawa wajen haifar da canji mai kyau ta hanyar gabatar da ayyuka masu ƙirƙira da tallafawa ci gaban malamai [34]. Don ƙirƙirar tsarin ilimi mai ƙarfi da amsawa, masu tsara manufofi dole ne su ɗauki matakai masu ƙarfi, kamar yin canje-canje a manufofi, sadaukar da albarkatu ga haɗakar fasaha, da ƙirƙirar tsare-tsare waɗanda ke haɓaka hanyoyin da ɗalibai ke mayar da hankali kan su. Waɗannan matakan suna da matuƙar muhimmanci don cimma sakamakon da ake so. Binciken da aka yi kwanan nan kan koyarwa daban-daban ya nuna a sarari cewa aiwatar da koyarwa daban-daban cikin nasara yana buƙatar ci gaba da horo da damar ci gaba ga malamai [35].
Wannan kayan aiki yana ba da tallafi mai mahimmanci ga masu koyar da ilimin hakori waɗanda ke son ɗaukar hanyar da ta dace da ɗalibai don tsara ayyukan ilmantarwa masu dacewa da ɗalibai. Duk da haka, wannan binciken ya takaita ne ga amfani da samfuran ML na yanke shawara. A nan gaba, ya kamata a tattara ƙarin bayanai don kwatanta aikin samfuran koyon injin daban-daban don kwatanta daidaito, aminci, da daidaiton kayan aikin shawarwari. Bugu da ƙari, lokacin zaɓar hanyar koyon injin da ta fi dacewa don wani aiki na musamman, yana da mahimmanci a yi la'akari da wasu abubuwa kamar sarkakiyar samfuri da fassararsa.
Iyakancewar wannan binciken shine cewa ya mayar da hankali ne kawai kan taswirar LS da IS tsakanin ɗaliban likitan hakori. Saboda haka, tsarin shawarwarin da aka haɓaka zai ba da shawarar waɗanda suka dace da ɗaliban likitan hakori kawai. Canje-canje ya zama dole don amfani da ɗaliban ilimi na gaba ɗaya.
Sabuwar kayan aikin ba da shawara kan koyon injin da aka ƙirƙiro yana da ikon rarrabawa da daidaita LS na ɗalibai nan take zuwa ga IS mai dacewa, wanda hakan ya sa ya zama shirin farko na ilimin hakori don taimakawa masu koyar da ilimin hakori tsara ayyukan koyarwa da koyo masu dacewa. Ta amfani da tsarin raba bayanai, yana iya samar da shawarwari na musamman, adana lokaci, inganta dabarun koyarwa, tallafawa tsoma baki da aka yi niyya, da kuma haɓaka ci gaban ƙwararru. Aikace-aikacensa zai haɓaka hanyoyin da suka shafi ɗaliban ilimi kan ilimin hakori.
Gilak Jani Associated Press. Daidaita ko rashin daidaito tsakanin salon koyo na ɗalibi da salon koyarwa na malami. Int J Mod Education Computer Science. 2012;4(11):51–60. https://doi.org/10.5815/ijmecs.2012.11.05
Lokacin Saƙo: Afrilu-29-2024