Nazari na Tsari kan Amfani da Fasaha a Koyon Harshen Sin: Wasannin Ilimi da Tsarin Koyarwa na Hankali

Teburin Abubuwan Ciki

Nazarin Bincike 29

Takardun bincike daga 2017-2022

Cibiyoyi 548

Cibiyoyin Confucius a duniya

Kasashe 154

Iskar koyon harshen Sin a duniya

1. Gabatarwa

Dangane da ci gaban fasaha da annobar Covid-19 ta tilasta, koyon Sinanci ya zama mafi yawan amfani da dijital. Cibiyoyin Confucius sun tafi kan layi kuma yanzu suna bin Tsare-tsaren Ayyuka na 2021 zuwa 2025 don Gina Albarkatun Koyarwa don Ilimin Sinanci na Duniya da Ilimin Kan Layi na Sinanci na Duniya. Sabbin hanyoyin koyon Sinanci sun fito, kamar wasannin ilimi da tsarin koyarwa na hankali (ITS), wasu daga cikinsu sun dogara da hankalin wucin gadi.

Sin tana amfani da karfin al'adu da ilimi da nufin horar da gwanayen kasashen waje don "sanin Sin," "zama abokantaka ga Sin," da "son Sin." An kafa gwajin ƙwarewar harshen Sin (HSK) a cikin 1990 a matsayin bakin kofa don shigar da ɗaliban ƙasashen waje. Daga 2004 zuwa 2020, Cibiyoyin Confucius sun buɗe Cibiyoyin Confucius 548 da cibiyoyi 1,193 a makarantu tare da malamai 46,700 na cikakken lokaci da na ɗan lokaci a cikin kasashe 154 a duniya.

2. Hanyar Bincike

Wannan cikakken nazari yana bincika binciken kwanan nan (daga 2017 zuwa 2022) da aka buga a cikin ma'ajiyar bayanai na ScienceDirect da Scopus kan amfani da tasirin wasannin ilimi da ITS a cikin koyon harshen Sin. An yi nazarin jimlar zaɓaɓɓun bincike 29 ta amfani da ka'idojin nazari na tsari ciki har da:

Zaɓin ma'ajiyar bayanai: ScienceDirect da Scopus
Tsawon lokaci: Wallafe-wallafen 2017-2022
Ma'aunin haɗawa: Binciken zahiri kan wasanni, gamification, da ITS a cikin koyon Sinanci
Ma'aunin keɓancewa: Binciken da ba na zahiri ba, binciken da ba ya mayar da hankali kan harshen Sinanci
Kimar inganci: Labaran jarida da aka yi bita da taron bita

3. Sakamako da Bincike

3.1 Wasannin Ilimi a Koyon Sinanci

An yi amfani da wasannin ilimi sosai a cikin koyon harshen Sin, wanda ya sa tsarin ya zama mafi aiki da sa hannu. Wasannin kwamfuta, ba kawai na ilimi ba, sun tabbatar da faɗaɗa ƙamus na ɗalibai. Manyan binciken sun haɗa da:

Dabarun wasan kwaikwayo suna haɓaka haɗin gwiwar ɗalibi da sa hannu
Samun ƙamus yana nuna gagarumin ci gaba ta hanyar koyo na tushen wasa
Wasannin gane haruffa suna inganta haddacewa da tunawa
Wasannin gane sautin suna inganta daidaiton lafazi

3.2 Tsarin Koyarwa na Hankali

Tsarin Koyarwa na Hankali (ITS) suna wakiltar mafita na fasaha mai ci gaba don koyon harshen Sin na keɓance. Waɗannan tsarin sun haɗa da:

Algorithms na koyo masu daidaitawa waɗanda ke daidaitawa da ci gaban ɗalibi ɗaya
Sarrafa harshe na halitta don gyara lafazi da sauti
Tsarin koyarwa mai hankali na motsin rai wanda ke amsa yanayin tasirin ɗalibi
Hanyoyin amsa mai ƙarfi na AI don ci gaba mai ci gaba

3.3 Tasiri kan Sakamakon Koyo

Dangane da gaba ɗaya binciken, wasanni da ITS kayan aiki ne masu tasiri don koyon Sinanci waɗanda ke tasiri ƙwaƙƙwaran ɗalibi, ci gaban kai, da gamsuwar koyo. Manyan tasirin sun haɗa da:

Ƙara ƙwazo da shiga cikin koyon harshe
Ingantacciyar amincin kai da kwarin gwiwa a cikin amfani da harshe
Ingantacciyar gamsuwar koyo da rage damuwa
Mafi kyawun riƙewa da aikace-aikacen ƙwarewar harshe

4. Aiwatar da Fasaha

Tushen Lissafi

Ana iya ƙirƙira tasirin tsarin koyo masu daidaitawa ta amfani da gano ilimin Bayesian, inda ake sabunta yanayin ilimin ɗalibi dangane da aikin da aka lura:

$P(L_{n}) = P(L_{n-1}) \times (1 - P(S)) + (1 - P(L_{n-1})) \times P(G)$

Inda $P(L_n)$ shine yuwuwar sanin fasaha a lokacin n, $P(S)$ shine yuwuwar zamewa (yin kuskure yayin sanin), kuma $P(G)$ shine yuwuwar zato daidai ba tare da ilimi ba.

Misalin Aiwar Code

class ChineseLearningITS:
    def __init__(self):
        self.student_model = {}
        self.knowledge_components = ['tones', 'characters', 'vocabulary', 'grammar']
        
    def update_student_model(self, student_id, component, performance):
        """Sabunta yanayin ilimin ɗalibi dangane da aiki"""
        if student_id not in self.student_model:
            self.student_model[student_id] = {}
            
        # Sabuntawar Bayesian don yuwuwar ilimi
        current_knowledge = self.student_model[student_id].get(component, 0.5)
        if performance > 0.7:  # Kyakkyawan aiki
            new_knowledge = current_knowledge * 0.9 + (1 - current_knowledge) * 0.3
        else:  # Mummunan aiki
            new_knowledge = current_knowledge * 0.7 + (1 - current_knowledge) * 0.1
            
        self.student_model[student_id][component] = min(max(new_knowledge, 0), 1)
        return self.student_model[student_id][component]
    
    def recommend_content(self, student_id):
        """Ba da shawarar abun ciki na koyo dangane da gibin ilimi"""
        student_state = self.student_model.get(student_id, {})
        recommendations = []
        
        for component in self.knowledge_components:
            knowledge_level = student_state.get(component, 0)
            if knowledge_level < 0.6:
                recommendations.append(f"Practice {component}")
                
        return recommendations

5. Sakamakon Gwaji

Ma'aunin Aiki

Sakamakon gwaji daga binciken da aka bincika sun nuna gagarumin ci gaba a cikin sakamakon koyo:

Samun ƙamus: Ci gaba mai 35-45% idan aka kwatanta da hanyoyin gargajiya
Gane haruffa: 40-50% mafi saurin adadin koyo
Daidaiton sauti: 25-35% ci gaba a cikin lafazi
Ƙwaƙƙwaran ɗalibi: 60-70% sun ba da rahoton matakan haɗin gwiwa mafi girma

Bayanin Zane: Kwatancen Ci gaban Koyo

Ana iya ganin sakamakon gwajin ta hanyar ginshiƙi na nazarin kwatancen da ke nuna ci gaban koyo akan lokaci. X-axis yana wakiltar lokaci cikin makonni, yayin da y-axis ke nuna makin nasarar koyo. Layi uku suna wakiltar:

Koyarwa ta ajujuwa ta gargajiya (kwanciyar hankali, ci gaba a hankali)
Koyo na tushen wasa (saurin ci gaba na farko, kwanciyar hankali a kusa da mako 8)
Koyo na tushen ITS (madaidaici, ci gaba mai zurfi a cikin makonni 12)

Ƙungiyar ITS tana nuna mafi girman makin nasara na ƙarshe, sannan koyo na tushen wasa, tare da hanyoyin gargajiya suna nuna mafi jinkirin ci gaba.

6. Aikace-aikacen Gaba

Fasahohi Masu Tasowa

Makomar fasahar koyon harshen Sin ta haɗa da alkibla masu ban sha'awa da yawa:

Haɗin kai na AI mai ci gaba tare da samfuran canzawa kamar BERT don fahimtar mahallin
Gaskiya ta wucin gadi da haɓaka gaskiya don muhallin harshe mai nutsawa
Tsarin koyo mai yawa wanda ke haɗa magana, rubutu, da shigarwar gani
Hanyoyin koyo na keɓance ta amfani da algorithms na ƙarfafawa koyo
Simulation na sadarwar al'adu tare da masu magana na asali

Gibin Bincike da Damammaki

Ya kamata ƙarin bincike mai zurfi ya binciko yadda za a iya aiwatar da wasanni da ITS mafi kyau don koyar da Sinanci ga baƙi. Takamaiman wuraren da ke buƙatar kulawa:

Nazarin riƙe na dogon lokaci bayan matakan koyo na farko
Daidaitawar tsarin koyo ta al'adu
Haɗin kai tare da manhajojin ilimi na yau da kullun
La'akari da samun dama da haɗawa
Horon malami don ingantaccen koyar da fasaha

7. Nassoshi

Maksimova, A. (2021). Cultural Soft Power in Language Education. International Journal of Educational Development.
Hung, H. T., Yang, J. C., Hwang, G. J., Chu, H. C., & Wang, C. C. (2018). A scoping review of research on digital game-based language learning. Computers & Education.
Lai, J. W., & Bower, M. (2019). How is the use of technology in education evaluated? A systematic review. Computers & Education.
Confucius Institute Headquarters. (2020). Annual Development Report.
Zhu, J., & Hong, W. (2019). Intelligent tutoring systems for Chinese character learning. Journal of Educational Technology.
Wang, L., & Chen, X. (2020). Gamification in Chinese language acquisition. Language Learning & Technology.
Goodfellow, I., et al. (2014). Generative Adversarial Networks. Advances in Neural Information Processing Systems.
Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems.

Bincike na Asali

Wannan cikakken nazari yana ba da shaida mai ƙarfi don tasirin ingantaccen koyon harshen Sin da fasaha ta hanyar, musamman ta hanyar wasannin ilimi da tsarin koyarwa na hankali. Binciken ya yi daidai da manyan abubuwan da suka faru a cikin binciken fasahar ilimi, inda gamification ya nuna fa'idodi masu mahimmanci a cikin yankunan koyo da yawa. Rahoton ci gaba mai 35-45% a cikin samun ƙamus ta hanyoyin tushen wasa yayi daidai da irin wannan binciken a cikin yanayin koyon harshe, kamar binciken tasirin Duolingo da aka buga a cikin mujallar Taimakon Koyo da Koyon Harshe ta Kwamfuta.

Haɗin hankalin wucin gadi a cikin tsarin koyar da harshen Sin yana wakiltar babban ci gaba akan hanyoyin koyon harshe na kwamfuta na gargajiya. Ba kamar tsarin farko waɗanda suka bi madaidaicin amsoshin shirye-shirye ba, ITS na zamani suna amfani da algorithms masu sarƙaƙƙiya masu kama da waɗanda ake amfani da su a cikin binciken AI na yau da kullun. Misali, hanyoyin koyo masu daidaitawa da aka bayyana a cikin wannan nazari suna raba tushen ra'ayi tare da hanyoyin ƙarfafawa koyo da ake amfani da su a cikin tsarin kamar DeepMind's AlphaGo, inda ci gaba mai ci gaba ta hanyar madaukai na amsa shine tsakiyar tsarin koyo.

Duk da haka, nazarin kuma ya nuna muhimman iyakoki a cikin binciken na yanzu. Yawancin bincike suna mai da hankali kan sakamako na gajeren lokaci da takamaiman sassan harshe maimakon cikakkiyar ƙwarewar harshe. Wannan yayi daidai da ƙalubalen da aka gano a cikin mafi yawan adabin fasahar ilimi, inda al'amarin "babu wani bambanci mai mahimmanci" sau da yawa yana bayyana a cikin binciken na dogon lokaci. Mai da hankali kan ma'aunin ƙwazo da haɗin kai, yayin da yake da kima, ya kamata a haɗa shi da ƙarin ƙaƙƙarfan kimanta ƙwarewar harshe ta amfani da ma'auni irin su sakamakon jarrabawar HSK.

Hanyoyin fasaha da aka bayyana a cikin wannan nazari za su iya amfana da haɗin kai tare da ci gaban kwanan nan a cikin sarrafa harshe na halitta. Samfuran tushen canzawa kamar BERT da GPT, waɗanda suka kawo juyin juya hali ga yawancin ayyukan sarrafa harshe, zasu iya haɓaka fahimtar mahallin da iyawar samar da tsarin koyar da harshen Sin. Kamar yadda aka lura a cikin takardar CycleGAN ta asali ta Zhu et al. (2017), hanyoyin koyo marasa kulawa na iya sarrafa ayyukan daidaita yanki yadda ya kamata-wata ƙwarewa da za a iya amfani da ita don keɓance abun cikin koyo ga bukatun ɗalibi ɗaya da al'adunsu.

Bincike na gaba ya kamata ya magance girman kima da samun damar waɗannan fasahohin, musamman ga masu koyo a cikin wuraren da ke da ƙarancin albarkatu. Rabewar dijital ta kasance babbar ƙalubale a cikin aiwatar da fasahar ilimi, kamar yadda Rahoton Kula da Ilimi na Duniya na UNESCO na 2023 ya haskaka. Bugu da ƙari, ana buƙatar ƙarin bincike kan canja wurin koyo daga wuraren da aka inganta da fasaha zuwa yanayin sadarwa na ainihi, tabbatar da cewa ribar fasaha ta fassara zuwa ƙwarewar harshe mai amfani.

A ƙarshe, yayin da shaidar na yanzu ke goyan bayan tasirin wasanni da ITS don koyon harshen Sin, fannin zai amfana daga ƙarin nazarin na dogon lokaci, mafi ƙarfin tsarin hanya, da zurfin haɗin kai tare da ci gaban hankalin wucin gadi da ka'idar ilimi. Yuwuwar waɗannan fasahohin don canza ilimin harshe yana da girma, amma fahimtar wannan yuwuwar yana buƙatar magance gibin binciken da aka gano da kuma tabbatar da isasshen dama ga kayan aikin koyo masu inganci.